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Song JH, Tomihama RT, Roh D, Cabrera A, Dardik A, Kiang SC. Leveraging Artificial Intelligence to Optimize the Care of Peripheral Artery Disease Patients. Ann Vasc Surg 2024; 107:48-54. [PMID: 38582202 DOI: 10.1016/j.avsg.2023.11.057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2023] [Accepted: 11/23/2023] [Indexed: 04/08/2024]
Abstract
Peripheral artery disease is a major atherosclerotic disease that is associated with poor outcomes such as limb loss, cardiovascular morbidity, and death. Artificial intelligence (AI) has seen increasing integration in medicine, and its various applications can optimize the care of peripheral artery disease (PAD) patients in diagnosis, predicting patient outcomes, and imaging interpretation. In this review, we introduce various AI applications such as natural language processing, supervised machine learning, and deep learning, and we analyze the current literature in which these algorithms have been applied to PAD.
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Affiliation(s)
- Jee Hoon Song
- Division of Vascular Surgery, Department of Surgery, Linda University School of Medicine, Loma Linda, CA
| | - Roger T Tomihama
- Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA
| | - Daniel Roh
- Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA
| | - Andrew Cabrera
- Division of Vascular and Interventional Radiology, Department of Radiology, Linda University School of Medicine, Loma Linda, CA
| | - Alan Dardik
- Division of Vascular Surgery, Department of Surgery, Yale University School of Medicine, New Haven, CT
| | - Sharon C Kiang
- Division of Vascular Surgery, Department of Surgery, Linda University School of Medicine, Loma Linda, CA; Division of Vascular Surgery, Department of Surgery, VA Loma Linda Healthcare System, Loma Linda, CA.
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Kumari K, Pahuja SK, Kumar S. A Comprehensive Examination of ChatGPT's Contribution to the Healthcare Sector and Hepatology. Dig Dis Sci 2024:10.1007/s10620-024-08659-4. [PMID: 39354272 DOI: 10.1007/s10620-024-08659-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/02/2024] [Accepted: 09/20/2024] [Indexed: 10/03/2024]
Abstract
Artificial Intelligence and Natural Language Processing technology have demonstrated significant promise across several domains within the medical and healthcare sectors. This technique has numerous uses in the field of healthcare. One of the primary challenges in implementing ChatGPT in healthcare is the requirement for precise and up-to-date data. In the case of the involvement of sensitive medical information, it is imperative to carefully address concerns regarding privacy and security when using GPT in the healthcare sector. This paper outlines ChatGPT and its relevance in the healthcare industry. It discusses the important aspects of ChatGPT's workflow and highlights the usual features of ChatGPT specifically designed for the healthcare domain. The present review uses the ChatGPT model within the research domain to investigate disorders associated with the hepatic system. This review demonstrates the possible use of ChatGPT in supporting researchers and clinicians in analyzing and interpreting liver-related data, thereby improving disease diagnosis, prognosis, and patient care.
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Affiliation(s)
- Kabita Kumari
- Department of Instrumentation and Control Engineering, Dr B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India.
| | - Sharvan Kumar Pahuja
- Department of Instrumentation and Control Engineering, Dr B. R. Ambedkar National Institute of Technology, Jalandhar, Punjab, 144011, India
| | - Sanjeev Kumar
- Biomedical Instrumentation Unit, CSIR-Central Scientific Instruments Organisation (CSIR-CSIO), Chandigarh, India
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Boutet A, Haile SS, Yang AZ, Son HJ, Malik M, Pai V, Nasralla M, Germann J, Vetkas A, Khalvati F, Ertl-Wagner BB. Assessing the Emergence and Evolution of Artificial Intelligence and Machine Learning Research in Neuroradiology. AJNR Am J Neuroradiol 2024; 45:1269-1275. [PMID: 38521092 PMCID: PMC11392363 DOI: 10.3174/ajnr.a8252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2023] [Accepted: 03/19/2024] [Indexed: 03/25/2024]
Abstract
BACKGROUND AND PURPOSE Interest in artificial intelligence (AI) and machine learning (ML) has been growing in neuroradiology, but there is limited knowledge on how this interest has manifested into research and specifically, its qualities and characteristics. This study aims to characterize the emergence and evolution of AI/ML articles within neuroradiology and provide a comprehensive overview of the trends, challenges, and future directions of the field. MATERIALS AND METHODS We performed a bibliometric analysis of the American Journal of Neuroradiology; the journal was queried for original research articles published since inception (January 1, 1980) to December 3, 2022 that contained any of the following key terms: "machine learning," "artificial intelligence," "radiomics," "deep learning," "neural network," "generative adversarial network," "object detection," or "natural language processing." Articles were screened by 2 independent reviewers, and categorized into statistical modeling (type 1), AI/ML development (type 2), both representing developmental research work but without a direct clinical integration, or end-user application (type 3), which is the closest surrogate of potential AI/ML integration into day-to-day practice. To better understand the limiting factors to type 3 articles being published, we analyzed type 2 articles as they should represent the precursor work leading to type 3. RESULTS A total of 182 articles were identified with 79% being nonintegration focused (type 1 n = 53, type 2 n = 90) and 21% (n = 39) being type 3. The total number of articles published grew roughly 5-fold in the last 5 years, with the nonintegration focused articles mainly driving this growth. Additionally, a minority of type 2 articles addressed bias (22%) and explainability (16%). These articles were primarily led by radiologists (63%), with most (60%) having additional postgraduate degrees. CONCLUSIONS AI/ML publications have been rapidly increasing in neuroradiology with only a minority of this growth being attributable to end-user application. Areas identified for improvement include enhancing the quality of type 2 articles, namely external validation, and addressing both bias and explainability. These results ultimately provide authors, editors, clinicians, and policymakers important insights to promote a shift toward integrating practical AI/ML solutions in neuroradiology.
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Affiliation(s)
- Alexandre Boutet
- From the Joint Department of Medical Imaging (A.B., M.N.), University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Samuel S Haile
- Temerty Faculty of Medicine (S.S.H., H.J.S., M.M.), University of Toronto, Toronto, Ontario, Canada
| | - Andrew Z Yang
- Division of Neurosurgery, Department of Surgery (A.Z.Y., J.G., A.V.), Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Hyo Jin Son
- Temerty Faculty of Medicine (S.S.H., H.J.S., M.M.), University of Toronto, Toronto, Ontario, Canada
| | - Mikail Malik
- Temerty Faculty of Medicine (S.S.H., H.J.S., M.M.), University of Toronto, Toronto, Ontario, Canada
| | - Vivek Pai
- Division of Neuroradiology, Department of Diagnostic Imaging (V.P., B.B.E.-W.), The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (V.P., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
| | - Mehran Nasralla
- From the Joint Department of Medical Imaging (A.B., M.N.), University Health Network, University of Toronto, Toronto, Ontario, Canada
| | - Jurgen Germann
- Division of Neurosurgery, Department of Surgery (A.Z.Y., J.G., A.V.), Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Artur Vetkas
- Division of Neurosurgery, Department of Surgery (A.Z.Y., J.G., A.V.), Toronto Western Hospital, University of Toronto, Toronto, Ontario, Canada
| | - Farzad Khalvati
- Department of Medical Imaging (V.P., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- Neurosciences and Mental Health Program (F.K., B.B.E.-W.), SickKids Research Institute, Toronto, Ontario, Canada
- Department of Computer Science (F.K.), University of Toronto, Toronto, Ontario, Canada
- Department of Mechanical and Industrial Engineering (F.K.), University of Toronto, Toronto, Ontario, Canada
| | - Birgit B Ertl-Wagner
- Division of Neuroradiology, Department of Diagnostic Imaging (V.P., B.B.E.-W.), The Hospital for Sick Children, Toronto, Ontario, Canada
- Department of Medical Imaging (V.P., F.K., B.B.E.-W.), University of Toronto, Toronto, Ontario, Canada
- Neurosciences and Mental Health Program (F.K., B.B.E.-W.), SickKids Research Institute, Toronto, Ontario, Canada
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Hassan M, Kushniruk A, Borycki E. Barriers to and Facilitators of Artificial Intelligence Adoption in Health Care: Scoping Review. JMIR Hum Factors 2024; 11:e48633. [PMID: 39207831 PMCID: PMC11393514 DOI: 10.2196/48633] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 02/28/2024] [Accepted: 06/12/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) use cases in health care are on the rise, with the potential to improve operational efficiency and care outcomes. However, the translation of AI into practical, everyday use has been limited, as its effectiveness relies on successful implementation and adoption by clinicians, patients, and other health care stakeholders. OBJECTIVE As adoption is a key factor in the successful proliferation of an innovation, this scoping review aimed at presenting an overview of the barriers to and facilitators of AI adoption in health care. METHODS A scoping review was conducted using the guidance provided by the Joanna Briggs Institute and the framework proposed by Arksey and O'Malley. MEDLINE, IEEE Xplore, and ScienceDirect databases were searched to identify publications in English that reported on the barriers to or facilitators of AI adoption in health care. This review focused on articles published between January 2011 and December 2023. The review did not have any limitations regarding the health care setting (hospital or community) or the population (patients, clinicians, physicians, or health care administrators). A thematic analysis was conducted on the selected articles to map factors associated with the barriers to and facilitators of AI adoption in health care. RESULTS A total of 2514 articles were identified in the initial search. After title and abstract reviews, 50 (1.99%) articles were included in the final analysis. These articles were reviewed for the barriers to and facilitators of AI adoption in health care. Most articles were empirical studies, literature reviews, reports, and thought articles. Approximately 18 categories of barriers and facilitators were identified. These were organized sequentially to provide considerations for AI development, implementation, and the overall structure needed to facilitate adoption. CONCLUSIONS The literature review revealed that trust is a significant catalyst of adoption, and it was found to be impacted by several barriers identified in this review. A governance structure can be a key facilitator, among others, in ensuring all the elements identified as barriers are addressed appropriately. The findings demonstrate that the implementation of AI in health care is still, in many ways, dependent on the establishment of regulatory and legal frameworks. Further research into a combination of governance and implementation frameworks, models, or theories to enhance trust that would specifically enable adoption is needed to provide the necessary guidance to those translating AI research into practice. Future research could also be expanded to include attempts at understanding patients' perspectives on complex, high-risk AI use cases and how the use of AI applications affects clinical practice and patient care, including sociotechnical considerations, as more algorithms are implemented in actual clinical environments.
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Affiliation(s)
- Masooma Hassan
- Department of Health Information Science, University of Victoria, Victoria, BC, Canada
| | - Andre Kushniruk
- Department of Health Information Science, University of Victoria, Victoria, BC, Canada
| | - Elizabeth Borycki
- Department of Health Information Science, University of Victoria, Victoria, BC, Canada
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Carmona-Rocha E, Rusiñol L, García-Melendo C, Iznardo H, Mozos A, López-Sánchez C, Yélamos O. Lentigo maligna: a comprehensive review on diagnosis and treatment. Ital J Dermatol Venerol 2024; 159:390-411. [PMID: 39069838 DOI: 10.23736/s2784-8671.24.07837-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/30/2024]
Abstract
Lentigo maligna (LM), a form of melanoma in situ, and LM melanoma (LMM), its invasive counterpart, exhibit distinctive epidemiology, risk factors, and clinical features compared to other melanoma subtypes. Notably, LM occurs on chronically sun-damaged skin presenting as a slow-growing, ill-defined patch which makes it difficult to diagnose and to treat. Additionally, while LM generally presents a favourable prognosis, it can also lead to dermal invasion and behave similarly to other melanomas with the same Breslow thickness. Hence, surgery continues to be the cornerstone treatment. Wide excisions are often necessary, but challenges arise when these lesions manifest in cosmetically sensitive regions, limiting the feasibility and desirability of large excisions. Specialized approaches, including margin-controlled surgery and image-guided treatment with reflectance confocal microscopy, have been developed to address these issues. Other non-surgical treatments such as cryosurgery, imiquimod, radiotherapy, or photodynamic therapy, may also be used but commonly present with recurrent/persistent disease. Herein we comprehensively review the existing literature on the management of LM/LMM, and discus the potential new advances on managing this challenging skin cancer.
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Affiliation(s)
- Elena Carmona-Rocha
- Dermatology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Institut de Recerca Sant Pau (IR Sant Pau), Barcelona, Spain
- Autonomous University of Barcelona, Bellaterra, Spain
| | - Lluís Rusiñol
- Dermatology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Institut de Recerca Sant Pau (IR Sant Pau), Barcelona, Spain
- Autonomous University of Barcelona, Bellaterra, Spain
- Dermatology Department, Teknon Quirónsalud Medical Center, Barcelona, Spain
| | | | - Helena Iznardo
- Dermatology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Institut de Recerca Sant Pau (IR Sant Pau), Barcelona, Spain
- Autonomous University of Barcelona, Bellaterra, Spain
| | - Anna Mozos
- Anatomical Pathology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
| | - Cristina López-Sánchez
- Dermatology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain
- Institut de Recerca Sant Pau (IR Sant Pau), Barcelona, Spain
- Autonomous University of Barcelona, Bellaterra, Spain
- Dermatology Department, Teknon Quirónsalud Medical Center, Barcelona, Spain
| | - Oriol Yélamos
- Dermatology Department, Hospital de la Santa Creu i Sant Pau, Barcelona, Spain -
- Institut de Recerca Sant Pau (IR Sant Pau), Barcelona, Spain
- Autonomous University of Barcelona, Bellaterra, Spain
- Dermatology Department, Teknon Quirónsalud Medical Center, Barcelona, Spain
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Sharma A, Al-Haidose A, Al-Asmakh M, Abdallah AM. Integrating Artificial Intelligence into Biomedical Science Curricula: Advancing Healthcare Education. Clin Pract 2024; 14:1391-1403. [PMID: 39051306 PMCID: PMC11270210 DOI: 10.3390/clinpract14040112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 06/28/2024] [Accepted: 07/04/2024] [Indexed: 07/27/2024] Open
Abstract
The integration of artificial intelligence (AI) into healthcare practice has improved patient management and care. Many clinical laboratory specialties have already integrated AI in diagnostic specialties such as radiology and pathology, where it can assist in image analysis, diagnosis, and clinical reporting. As AI technologies continue to advance, it is crucial for biomedical science students to receive comprehensive education and training in AI concepts and applications and to understand the ethical consequences for such development. This review focus on the importance of integrating AI into biomedical science curricula and proposes strategies to enhance curricula for different specialties to prepare future healthcare workers. Improving the curriculum can be achieved by introducing specific subjects related to AI such as informatics, data sciences, and digital health. However, there are many challenges to enhancing the curriculum with AI. In this narrative review, we discuss these challenges and suggest mitigation strategies.
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Affiliation(s)
- Aarti Sharma
- College of Health Sciences, QU Health Sector, Qatar University, Doha 2713, Qatar
| | - Amal Al-Haidose
- Department of Biomedical Sciences, College of Health Sciences, QU Health Sector, Qatar University, Doha 2713, Qatar
| | - Maha Al-Asmakh
- Department of Biomedical Sciences, College of Health Sciences, QU Health Sector, Qatar University, Doha 2713, Qatar
| | - Atiyeh M. Abdallah
- Department of Biomedical Sciences, College of Health Sciences, QU Health Sector, Qatar University, Doha 2713, Qatar
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Vandemeulebroucke T. The ethics of artificial intelligence systems in healthcare and medicine: from a local to a global perspective, and back. Pflugers Arch 2024:10.1007/s00424-024-02984-3. [PMID: 38969841 DOI: 10.1007/s00424-024-02984-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Revised: 04/30/2024] [Accepted: 06/24/2024] [Indexed: 07/07/2024]
Abstract
Artificial intelligence systems (ai-systems) (e.g. machine learning, generative artificial intelligence), in healthcare and medicine, have been received with hopes of better care quality, more efficiency, lower care costs, etc. Simultaneously, these systems have been met with reservations regarding their impacts on stakeholders' privacy, on changing power dynamics, on systemic biases, etc. Fortunately, healthcare and medicine have been guided by a multitude of ethical principles, frameworks, or approaches, which also guide the use of ai-systems in healthcare and medicine, in one form or another. Nevertheless, in this article, I argue that most of these approaches are inspired by a local isolationist view on ai-systems, here exemplified by the principlist approach. Despite positive contributions to laying out the ethical landscape of ai-systems in healthcare and medicine, such ethics approaches are too focused on a specific local healthcare and medical setting, be it a particular care relationship, a particular care organisation, or a particular society or region. By doing so, they lose sight of the global impacts ai-systems have, especially environmental impacts and related social impacts, such as increased health risks. To meet this gap, this article presents a global approach to the ethics of ai-systems in healthcare and medicine which consists of five levels of ethical impacts and analysis: individual-relational, organisational, societal, global, and historical. As such, this global approach incorporates the local isolationist view by integrating it in a wider landscape of ethical consideration so to ensure ai-systems meet the needs of everyone everywhere.
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Affiliation(s)
- Tijs Vandemeulebroucke
- Bonn Sustainable AI Lab, Institut für Wissenschaft und Ethik, Universität Bonn-University of Bonn, Bonner Talweg 57, 53113, Bonn, Germany.
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Aldousari E, Kithinji D. Artificial intelligence and health information: A bibliometric analysis of three decades of research. Health Informatics J 2024; 30:14604582241283969. [PMID: 39262107 DOI: 10.1177/14604582241283969] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/13/2024]
Abstract
Information on the application of artificial intelligence (AI) in healthcare is needed to align healthcare transformation efforts. This bibliometric analysis aims to establish the patterns of publication activities on the application of AI in health. A total of 1083 scholarly papers published between 1993 and 2023 were retrieved from the Web of Science and Scopus databases. R Studio and VOSviewer were applied to quantify and illustrate publication patterns and citation rates. Publication rates grew by an average rate of 13% yearly, with each document being cited averagely 12 times. The articles had a mean of five co-authors, with a global co-authorship rate of 10%. COVID-19, artificial intelligence, and machine learning dominated the publications. The US, China, UK, Canada, and India coordinated most of the collaborative research. AI-based health information research is growing steadily. International collaborations can be leveraged to ensure the spread and interoperability of AI-based healthcare innovations globally.
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Affiliation(s)
- Elham Aldousari
- Kuwait University College of Allied Health Sciences, Kuwait City, Kuwait
- Kenyatta University School of Medicine, Nairobi, Kenya
| | - Dennis Kithinji
- Kuwait University College of Allied Health Sciences, Kuwait City, Kuwait
- Kenyatta University School of Medicine, Nairobi, Kenya
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Mahesh N, Devishamani CS, Raghu K, Mahalingam M, Bysani P, Chakravarthy AV, Raman R. Advancing healthcare: the role and impact of AI and foundation models. Am J Transl Res 2024; 16:2166-2179. [PMID: 39006256 PMCID: PMC11236664 DOI: 10.62347/wqwv9220] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2024] [Accepted: 05/06/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND The integration of artificial intelligence (AI) into the healthcare domain is a monumental shift with profound implications for diagnostics, medical interventions, and the overall structure of healthcare systems. PURPOSE This study explores the transformative journey of foundation AI models in healthcare, shedding light on the challenges, ethical considerations, and vast potential they hold for improving patient outcome and system efficiency. Notably, in this investigation we observe a relatively slow adoption of AI within the public sector of healthcare. The evolution of AI in healthcare is un-paralleled, especially its prowess in revolutionizing diagnostic processes. RESULTS This research showcases how these foundational models can unravel hidden patterns within complex medical datasets. The impact of AI reverberates through medical interventions, encompassing pathology, imaging, genomics, and personalized healthcare, positioning AI as a cornerstone in the quest for precision medicine. The paper delves into the applications of generative AI models in critical facets of healthcare, including decision support, medical imaging, and the prediction of protein structures. The study meticulously evaluates various AI models, such as transfer learning, RNN, autoencoders, and their roles in the healthcare landscape. A pioneering concept introduced in this exploration is that of General Medical AI (GMAI), advocating for the development of reusable and flexible AI models. CONCLUSION The review article discusses how AI can revolutionize healthcare by stressing the significance of transparency, fairness and accountability, in AI applications regarding patient data privacy and biases. By tackling these issues and suggesting a governance structure the article adds to the conversation about AI integration in healthcare environments.
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Affiliation(s)
- Nandhini Mahesh
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
| | - Chitralekha S Devishamani
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
| | - Keerthana Raghu
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
| | - Maanasi Mahalingam
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
| | - Pragathi Bysani
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
| | | | - Rajiv Raman
- Shri Bhagwan Mahavir Vitreoretinal Services, Sankara Nethralaya, Medical Research Foundation Chennai, Tamil Nadu, India
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Attia H, Rejaibi S, Wechtati G, Mrabet A, Borsali-Falfoul N, Chadli A. Doctoral theses at the medical faculty of Tunis from 2015 to 2017: Scientific publication rates and associated factors. LA TUNISIE MEDICALE 2024; 102:337-342. [PMID: 38864196 PMCID: PMC11387993 DOI: 10.62438/tunismed.v102i6.4958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/25/2024] [Accepted: 04/21/2024] [Indexed: 06/13/2024]
Abstract
INTRODUCTION Medical doctoral thesis publication is a way to ensure knowledge dissemination and to increase the scientific research visibility. AIM To determine thesis-related publication's rate at the Faculty of Medicine of Tunis (FMT), and identify associated factors. METHODS Cross-sectional retrospective study including medical theses registered at the FMT over the study period (2015-2017). All publications related to the defended thesis were collated by scanning Scopus and Google scholar databases, up to April 2022. Binary logistic regression was performed to assess associated factors to publication. Adjusted Odds Ratios (AOR) were presented with 95% confidence interval. RESULTS Out of 878 defended theses, 11.8% (n=104) were published. Out of 130 publications in total, 90 (69.2%) interested Scopus-indexed journals with a mean Scimago Journal Rank (SJR) of 0.70. The publication was in English in 73.1% of cases. The median time between the thesis defense and the first scientific publication was 15 months. In multivariable analysis, associated factors to "at least one thesis-related publication" were the resident status of the candidate (AOR=2.35 [1.2-4.7]) and the grade assistant professor of the thesis supervisor (AOR=2.48 [1.1-5.6]). CONCLUSION Compared to the number of defended theses, the thesis-related publication's rate at the FMT is relatively low. Thus, enhanced support for doctoral students to optimize their engagement in research and to consequently promote scientific publication is highly recommended.
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Affiliation(s)
- Hajer Attia
- Physical Medicine Department, Principal Military Hospital, Tunis-Tunisia
| | - Salsabil Rejaibi
- Faculty of Medicine of Tunis-Tunis El Manar University, Tunis-Tunisia
- Direction of primary healthcare services (DSSB), Tunis-Tunisia
| | - Ghada Wechtati
- Physical Medicine Department, Principal Military Hospital, Tunis-Tunisia
| | - Ali Mrabet
- Faculty of Medicine of Tunis-Tunis El Manar University, Tunis-Tunisia
- Ministry of Health, Tunis-Tunisia
| | - Nebiha Borsali-Falfoul
- Faculty of Medicine of Tunis-Tunis El Manar University, Tunis-Tunisia
- Intensive Care Unit, Habib Thameur University Hospital, Tunis-Tunisia
| | - Aschraf Chadli
- Faculty of Medicine of Tunis-Tunis El Manar University, Tunis-Tunisia
- Pathology Department, Habib Thameur University Hospital, Tunis-Tunisia
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Nadal E, Benito E, Ródenas-Navarro AM, Palanca A, Martinez-Hervas S, Civera M, Ortega J, Alabadi B, Piqueras L, Ródenas JJ, Real JT. Machine Learning Model in Obesity to Predict Weight Loss One Year after Bariatric Surgery: A Pilot Study. Biomedicines 2024; 12:1175. [PMID: 38927382 PMCID: PMC11200726 DOI: 10.3390/biomedicines12061175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2024] [Revised: 05/13/2024] [Accepted: 05/21/2024] [Indexed: 06/28/2024] Open
Abstract
Roux-en-Y gastric bypass (RYGB) is a treatment for severe obesity. However, many patients have insufficient total weight loss (TWL) after RYGB. Although multiple factors have been involved, their influence is incompletely known. The aim of this exploratory study was to evaluate the feasibility and reliability of the use of machine learning (ML) techniques to estimate the success in weight loss after RYGP, based on clinical, anthropometric and biochemical data, in order to identify morbidly obese patients with poor weight responses. We retrospectively analyzed 118 patients, who underwent RYGB at the Hospital Clínico Universitario of Valencia (Spain) between 2013 and 2017. We applied a ML approach using local linear embedding (LLE) as a tool for the evaluation and classification of the main parameters in conjunction with evolutionary algorithms for the optimization and adjustment of the parameter model. The variables associated with one-year postoperative %TWL were obstructive sleep apnea, osteoarthritis, insulin treatment, preoperative weight, insulin resistance index, apolipoprotein A, uric acid, complement component 3, and vitamin B12. The model correctly classified 71.4% of subjects with TWL < 30% although 36.4% with TWL ≥ 30% were incorrectly classified as "unsuccessful procedures". The ML-model processed moderate discriminatory precision in the validation set. Thus, in severe obesity, ML-models can be useful to assist in the selection of patients before bariatric surgery.
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Affiliation(s)
- Enrique Nadal
- Instituto Universitario de Ingeniería Mecánica y Biomecánica (I2MB), Universitat Politècnica de València, 46022 Valencia, Spain;
| | - Esther Benito
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain; (E.B.); (B.A.); (L.P.); (J.T.R.)
| | - Ana María Ródenas-Navarro
- Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain; (A.M.R.-N.); (A.P.); (M.C.)
| | - Ana Palanca
- Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain; (A.M.R.-N.); (A.P.); (M.C.)
- INCLIVA Biomedical Research Institute, 46010 Valencia, Spain;
| | - Sergio Martinez-Hervas
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain; (E.B.); (B.A.); (L.P.); (J.T.R.)
- Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain; (A.M.R.-N.); (A.P.); (M.C.)
- INCLIVA Biomedical Research Institute, 46010 Valencia, Spain;
- Department of Medicine, University of Valencia, 46010 Valencia, Spain
| | - Miguel Civera
- Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain; (A.M.R.-N.); (A.P.); (M.C.)
- INCLIVA Biomedical Research Institute, 46010 Valencia, Spain;
| | - Joaquín Ortega
- INCLIVA Biomedical Research Institute, 46010 Valencia, Spain;
- General Surgery Service, University Hospital of Valencia, 46010 Valencia, Spain
- Department of Surgery, University of Valencia, 46010 Valencia, Spain
| | - Blanca Alabadi
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain; (E.B.); (B.A.); (L.P.); (J.T.R.)
- Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain; (A.M.R.-N.); (A.P.); (M.C.)
- INCLIVA Biomedical Research Institute, 46010 Valencia, Spain;
| | - Laura Piqueras
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain; (E.B.); (B.A.); (L.P.); (J.T.R.)
- INCLIVA Biomedical Research Institute, 46010 Valencia, Spain;
- Department of Pharmacology, University of Valencia, 46010 Valencia, Spain
| | - Juan José Ródenas
- Instituto Universitario de Ingeniería Mecánica y Biomecánica (I2MB), Universitat Politècnica de València, 46022 Valencia, Spain;
| | - José T. Real
- CIBER de Diabetes y Enfermedades Metabólicas Asociadas (CIBERDEM), Instituto de Salud Carlos III (ISCIII), 28040 Madrid, Spain; (E.B.); (B.A.); (L.P.); (J.T.R.)
- Endocrinology and Nutrition Service, Clinical University Hospital of Valencia, 46010 Valencia, Spain; (A.M.R.-N.); (A.P.); (M.C.)
- INCLIVA Biomedical Research Institute, 46010 Valencia, Spain;
- Department of Medicine, University of Valencia, 46010 Valencia, Spain
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Daniyal M, Qureshi M, Marzo RR, Aljuaid M, Shahid D. Exploring clinical specialists' perspectives on the future role of AI: evaluating replacement perceptions, benefits, and drawbacks. BMC Health Serv Res 2024; 24:587. [PMID: 38725039 PMCID: PMC11080164 DOI: 10.1186/s12913-024-10928-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2023] [Accepted: 03/29/2024] [Indexed: 05/13/2024] Open
Abstract
BACKGROUND OF STUDY Over the past few decades, the utilization of Artificial Intelligence (AI) has surged in popularity, and its application in the medical field is witnessing a global increase. Nevertheless, the implementation of AI-based healthcare solutions has been slow in developing nations like Pakistan. This unique study aims to assess the opinion of clinical specialists on the future replacement of AI, its associated benefits, and its drawbacks in form southern region of Pakistan. MATERIAL AND METHODS A cross-sectional selective study was conducted from 140 clinical specialists (Surgery = 24, Pathology = 31, Radiology = 35, Gynecology = 35, Pediatric = 17) from the neglected southern Punjab region of Pakistan. The study was analyzed using χ2 - the test of association and the nexus between different factors was examined by multinomial logistic regression. RESULTS Out of 140 respondents, 34 (24.3%) believed hospitals were ready for AI, while 81 (57.9%) disagreed. Additionally, 42(30.0%) were concerned about privacy violations, and 70(50%) feared AI could lead to unemployment. Specialists with less than 6 years of experience are more likely to embrace AI (p = 0.0327, OR = 3.184, 95% C.I; 0.262, 3.556) and those who firmly believe that AI knowledge will not replace their future tasks exhibit a lower likelihood of accepting AI (p = 0.015, OR = 0.235, 95% C.I: (0.073, 0.758). Clinical specialists who perceive AI as a technology that encompasses both drawbacks and benefits demonstrated a higher likelihood of accepting its adoption (p = 0.084, OR = 2.969, 95% C.I; 0.865, 5.187). CONCLUSION Clinical specialists have embraced AI as the future of the medical field while acknowledging concerns about privacy and unemployment.
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Affiliation(s)
- Muhammad Daniyal
- Department of Statistics, Faculty of Computing, Islamia University of Bahawalpur, Bahawalpur, Pakistan.
| | - Moiz Qureshi
- Government Degree College, TandoJam, Hyderabad, Sindh, Pakistan
| | - Roy Rillera Marzo
- Faculty of Humanities and Health Sciences, Curtin University, Malaysia, , Miri, Sarawak, Malaysia
- Jeffrey Cheah School of Medicine and Health Sciences, Global Public Health, Monash University Malaysia, Subang Jaya, Selangor, Malaysia
| | - Mohammed Aljuaid
- Department of Health Administration, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
| | - Duaa Shahid
- Hult International Business School, 02141, Cambridge, MA, USA
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Bellos T, Manolitsis I, Katsimperis S, Juliebø-Jones P, Feretzakis G, Mitsogiannis I, Varkarakis I, Somani BK, Tzelves L. Artificial Intelligence in Urologic Robotic Oncologic Surgery: A Narrative Review. Cancers (Basel) 2024; 16:1775. [PMID: 38730727 PMCID: PMC11083167 DOI: 10.3390/cancers16091775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2024] [Revised: 04/29/2024] [Accepted: 05/02/2024] [Indexed: 05/13/2024] Open
Abstract
With the rapid increase in computer processing capacity over the past two decades, machine learning techniques have been applied in many sectors of daily life. Machine learning in therapeutic settings is also gaining popularity. We analysed current studies on machine learning in robotic urologic surgery. We searched PubMed/Medline and Google Scholar up to December 2023. Search terms included "urologic surgery", "artificial intelligence", "machine learning", "neural network", "automation", and "robotic surgery". Automatic preoperative imaging, intraoperative anatomy matching, and bleeding prediction has been a major focus. Early artificial intelligence (AI) therapeutic outcomes are promising. Robot-assisted surgery provides precise telemetry data and a cutting-edge viewing console to analyse and improve AI integration in surgery. Machine learning enhances surgical skill feedback, procedure effectiveness, surgical guidance, and postoperative prediction. Tension-sensors on robotic arms and augmented reality can improve surgery. This provides real-time organ motion monitoring, improving precision and accuracy. As datasets develop and electronic health records are used more and more, these technologies will become more effective and useful. AI in robotic surgery is intended to improve surgical training and experience. Both seek precision to improve surgical care. AI in ''master-slave'' robotic surgery offers the detailed, step-by-step examination of autonomous robotic treatments.
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Affiliation(s)
- Themistoklis Bellos
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Ioannis Manolitsis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Stamatios Katsimperis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | | | - Georgios Feretzakis
- School of Science and Technology, Hellenic Open University, 26335 Patras, Greece;
| | - Iraklis Mitsogiannis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Ioannis Varkarakis
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
| | - Bhaskar K. Somani
- Department of Urology, University of Southampton, Southampton SO16 6YD, UK;
| | - Lazaros Tzelves
- 2nd Department of Urology, Sismanoglio General Hospital of Athens, 15126 Athens, Greece; (T.B.); (I.M.); (S.K.); (I.M.); (I.V.)
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Dai C, Xiong Y, Zhu P, Yao L, Lin J, Yao J, Zhang X, Huang R, Wang R, Hou J, Wang K, Shi Z, Chen F, Guo J, Zeng M, Zhou J, Wang S. Deep Learning Assessment of Small Renal Masses at Contrast-enhanced Multiphase CT. Radiology 2024; 311:e232178. [PMID: 38742970 DOI: 10.1148/radiol.232178] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Background Accurate characterization of suspicious small renal masses is crucial for optimized management. Deep learning (DL) algorithms may assist with this effort. Purpose To develop and validate a DL algorithm for identifying benign small renal masses at contrast-enhanced multiphase CT. Materials and Methods Surgically resected renal masses measuring 3 cm or less in diameter at contrast-enhanced CT were included. The DL algorithm was developed by using retrospective data from one hospital between 2009 and 2021, with patients randomly allocated in a training and internal test set ratio of 8:2. Between 2013 and 2021, external testing was performed on data from five independent hospitals. A prospective test set was obtained between 2021 and 2022 from one hospital. Algorithm performance was evaluated by using the area under the receiver operating characteristic curve (AUC) and compared with the results of seven clinicians using the DeLong test. Results A total of 1703 patients (mean age, 56 years ± 12 [SD]; 619 female) with a single renal mass per patient were evaluated. The retrospective data set included 1063 lesions (874 in training set, 189 internal test set); the multicenter external test set included 537 lesions (12.3%, 66 benign) with 89 subcentimeter (≤1 cm) lesions (16.6%); and the prospective test set included 103 lesions (13.6%, 14 benign) with 20 (19.4%) subcentimeter lesions. The DL algorithm performance was comparable with that of urological radiologists: for the external test set, AUC was 0.80 (95% CI: 0.75, 0.85) versus 0.84 (95% CI: 0.78, 0.88) (P = .61); for the prospective test set, AUC was 0.87 (95% CI: 0.79, 0.93) versus 0.92 (95% CI: 0.86, 0.96) (P = .70). For subcentimeter lesions in the external test set, the algorithm and urological radiologists had similar AUC of 0.74 (95% CI: 0.63, 0.83) and 0.81 (95% CI: 0.68, 0.92) (P = .78), respectively. Conclusion The multiphase CT-based DL algorithm showed comparable performance with that of radiologists for identifying benign small renal masses, including lesions of 1 cm or less. Published under a CC BY 4.0 license. Supplemental material is available for this article.
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Affiliation(s)
- Chenchen Dai
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Ying Xiong
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Pingyi Zhu
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Linpeng Yao
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jinglai Lin
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jiaxi Yao
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Xue Zhang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Risheng Huang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Run Wang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jun Hou
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Kang Wang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Zhang Shi
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Feng Chen
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jianming Guo
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Mengsu Zeng
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Jianjun Zhou
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
| | - Shuo Wang
- From the Departments of Radiology (C.D., P.Z., Z.S., M.Z., J.Z.), Urology (Y.X., J.G.), and Pathology (J.H.), Zhongshan Hospital, Fudan University, Shanghai, China; Shanghai Institute of Medical Imaging, Shanghai, China (C.D., P.Z., Z.S., M.Z.); Department of Radiology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China (L.Y., F.C.); Departments of Urology (J.L.) and Radiology (J.Z.), Xiamen Branch, Zhongshan Hospital, Fudan University, 668 Jinhu Road, Huli District, Xiamen 361015, China; Department of Urology, Zhangye People's Hospital affiliated to Hexi University, Zhangye, China (J.Y.); Department of Radiology, the First People's Hospital of Lianyungang, Lianyungang, China (X.Z.); Department of Radiology, Quanzhou First Hospital, Fujian Medical University, Quanzhou, China (R.H.); Department of Pathology, Sir Run Run Shaw Hospital, Hangzhou, China (R.W.); Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China (K.W., S.W.); Shanghai Key Laboratory of MICCAI, Shanghai, China (K.W., S.W.); Xiamen Municipal Clinical Research Center for Medical Imaging, Xiamen, China (J.Z.); and Xiamen Key Clinical Specialty, Xiamen, China (J.Z.)
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15
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Pak S, Park SG, Park J, Cho ST, Lee YG, Ahn H. Applications of artificial intelligence in urologic oncology. Investig Clin Urol 2024; 65:202-216. [PMID: 38714511 PMCID: PMC11076794 DOI: 10.4111/icu.20230435] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 02/24/2024] [Accepted: 03/11/2024] [Indexed: 05/10/2024] Open
Abstract
PURPOSE With the recent rising interest in artificial intelligence (AI) in medicine, many studies have explored the potential and usefulness of AI in urological diseases. This study aimed to comprehensively review recent applications of AI in urologic oncology. MATERIALS AND METHODS We searched the PubMed-MEDLINE databases for articles in English on machine learning (ML) and deep learning (DL) models related to general surgery and prostate, bladder, and kidney cancer. The search terms were a combination of keywords, including both "urology" and "artificial intelligence" with one of the following: "machine learning," "deep learning," "neural network," "renal cell carcinoma," "kidney cancer," "urothelial carcinoma," "bladder cancer," "prostate cancer," and "robotic surgery." RESULTS A total of 58 articles were included. The studies on prostate cancer were related to grade prediction, improved diagnosis, and predicting outcomes and recurrence. The studies on bladder cancer mainly used radiomics to identify aggressive tumors and predict treatment outcomes, recurrence, and survival rates. Most studies on the application of ML and DL in kidney cancer were focused on the differentiation of benign and malignant tumors as well as prediction of their grade and subtype. Most studies suggested that methods using AI may be better than or similar to existing traditional methods. CONCLUSIONS AI technology is actively being investigated in the field of urological cancers as a tool for diagnosis, prediction of prognosis, and decision-making and is expected to be applied in additional clinical areas soon. Despite technological, legal, and ethical concerns, AI will change the landscape of urological cancer management.
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Affiliation(s)
- Sahyun Pak
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Sung Gon Park
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | | | - Sung Tae Cho
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Young Goo Lee
- Department of Urology, Kangnam Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Korea
| | - Hanjong Ahn
- Department of Urology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
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16
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Dhanushkodi K, Vinayagasundaram P, Anbalagan V, Subbaraj S, Sethuraman R. TriKSV-LG: a robust approach to disease prediction in healthcare systems using AI and Levy Gazelle optimization. Comput Methods Biomech Biomed Engin 2024:1-17. [PMID: 38688507 DOI: 10.1080/10255842.2024.2339479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2023] [Accepted: 04/01/2024] [Indexed: 05/02/2024]
Abstract
A seamless connection between the Internet and people is provided by the Internet of Things (IoT). Furthermore, lives are enhanced using the integration of the cloud layer. In the healthcare domain, a reactive healthcare strategy is turned into a proactive one using predictive analysis. The challenges faced by existing techniques are inaccurate prediction and a time-consuming process. This paper introduces an Artificial Intelligence (AI) and IoT-based disease prediction method, the TriKernel Support Vector-based Levy Gazelle (TriKSV-LG) Algorithm, which aims to improve accuracy, and reduce the time of predicting diseases (kidney and heart) in healthcare systems. The IoT sensors collect information about patients' health conditions, and the AI employs the information in disease prediction. TriKSV utilizes multiple kernel functions, including linear, polynomial, and radial basis functions, to classify features more effectively. By learning from different representations of the data, TriKSV better handles variations and complexities within the dataset, leading to more robust disease prediction models. The Levy Flight strategy with Gazelle optimization algorithm tunes the hyperparameters and balances the exploration and exploitation for optimal hyperparameter configurations in predicting chronic kidney disease (CKD) and heart disease (HD). Furthermore, TriKSV's incorporation of multiple kernel functions, combined with the Gazelle optimization strategy, helps mitigate overfitting by providing a more comprehensive search space for optimal hyperparameter selection. The proposed TriKSV-LG method is applied to two different datasets, namely the CKD dataset and the HD dataset, and evaluated using performance measures such as AUC-ROC, specificity, F1-score, recall, precision, and accuracy. The results demonstrate that the proposed TriKSV-LG method achieved an accuracy of 98.56% in predicting kidney disease using the CKD dataset and 98.11% accuracy in predicting HD using the HD dataset.
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Affiliation(s)
- Kavitha Dhanushkodi
- School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, Tamil Nadu, India
| | - Prema Vinayagasundaram
- Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, India
| | - Vidhya Anbalagan
- Department of Computer Science and Engineering, SRM Valliammai Engineering College, Kattankulathur, India
| | - Surendran Subbaraj
- Department of Computer Science and Engineering, Tagore Engineering College, Chennai, Tamil Nadu, India
| | - Ravikumar Sethuraman
- Department of Computer Science and Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India
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17
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Alvitez-Temoche D, Silva H, Aguila ED, Mauricio F, Espinoza-Carhuancho F, Mayta-Tovalino F. Scientometric Analysis of the World Scientific Production on Augmented and Virtual Reality in Dental Education. J Contemp Dent Pract 2024; 25:358-364. [PMID: 38956852 DOI: 10.5005/jp-journals-10024-3675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/04/2024]
Abstract
AIM The aim of this study was to perform a comprehensive bibliometric analysis of virtual reality (VR) and augmented reality (AR) applications in dental education. MATERIALS AND METHODS A cross-sectional research was carried out using a bibliometric methodology. This process entailed the assessment of metadata from scientific publications that are catalogued in the Scopus database, covering the period from January 2018 to August 2023. A variety of indicators were utilized to scrutinize scientific production and dissemination within the academic community. These encompassed elements such as the author, the publication itself, the number of citations, institutional and collaborative affiliations, geographical location, journal quartile ranking, h-index, Source Normalized Impact per Paper (SNIP), Field-Weighted Citation Impact (FWCI), SCImago Journal Rank (SJR), and the CiteScore. RESULTS Several institutions from different countries and their academic output were found. Beihang University stands out with 16 scholarly articles, followed by Stanford University with 16 articles and 170 citations. The Q1 quartile has experienced a steady increase, reaching 87 scientific articles. The top 10 authors in scientific production on augmented and VR in dentistry include Joe Amal Cecil, Avinash Gupta, and Miguel A Pirela-Cruz. In terms of co-authorship by country, the United States, Germany, and China are the most predominant in the clusters represented. However, other clusters also have a significant presence. By analyzing the explored trends and themes of keyword co-occurrence, four main clusters were identified. The yellow cluster contained the largest amount of research with the keyword "virtual reality." In addition, the blue cluster was found to be best related to the green "simulation," purple "virtual reality (VR)," and light blue "human-centered computing" clusters. CONCLUSION This study evidenced the availability and quality of the data used for the analysis. Future studies could consider the use of VR systems with integrated eye tracking and compare their effect in dentistry during dental procedures. CLINICAL SIGNIFICANCE The clinical importance of this study lies in its potential to improve dental education. The VR and AR can provide dental students with immersive, hands-on learning experiences, which can enhance their understanding and clinical skills. Furthermore, the translational value of this study extends beyond dental education. The insights gained from this research could be applicable to other fields of medical education where hands-on training is crucial. Thus, the findings of this study have the potential to influence the broader landscape of medical education, ultimately leading to improved healthcare outcomes. How to cite this article: Alvitez-Temoche D, Silva H, Aguila ED, et al. Scientometric Analysis of the World Scientific Production on Augmented and Virtual Reality in Dental Education. J Contemp Dent Pract 2024;25(4):358-364.
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Affiliation(s)
- Daniel Alvitez-Temoche
- Faculty of Dentistry, Department of Academic, Universidad Nacional Federico Villarreal, Lima, Peru
| | - Herbert Silva
- Faculty of Dentistry, Department of Academic, Universidad Nacional Federico Villarreal, Lima, Peru
| | - Elca Del Aguila
- Faculty of Dentistry, Department of Academic, Universidad Nacional Federico Villarreal, Lima, Peru
| | - Franco Mauricio
- Faculty of Dentistry, Department of Academic, Universidad Nacional Federico Villarreal, Lima, Peru
| | - Fran Espinoza-Carhuancho
- Department of Academic, Grupo de Bibliometría, Evaluación de evidencia y Revisiones Sistemáticas (BEERS), Human Medicine Career, Universidad Cientifica del Sur, Lima, Peru
| | - Frank Mayta-Tovalino
- Department of Systematic Reviews and Meta-analysis Unit, Vicerrectorado de Investigación; Department of Postgraduate, Universidad San Ignacio de Loyola, Lima, Peru, Phone: +51 1317-1000, e-mail:
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18
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Giannitto C, Carnicelli G, Lusi S, Ammirabile A, Casiraghi E, De Virgilio A, Esposito AA, Farina D, Ferreli F, Franzese C, Frigerio GM, Lo Casto A, Malvezzi L, Lorini L, Othman AE, Preda L, Scorsetti M, Bossi P, Mercante G, Spriano G, Balzarini L, Francone M. The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey. J Pers Med 2024; 14:341. [PMID: 38672968 PMCID: PMC11050769 DOI: 10.3390/jpm14040341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.
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Affiliation(s)
- Caterina Giannitto
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giorgia Carnicelli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Stefano Lusi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Elena Casiraghi
- Department of Computer Science “Giovanni degli Antoni”, University of Milan, Via Celoria 18, 20133 Milan, Italy;
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 717 Potter Street, Berkeley, CA 94710, USA
| | - Armando De Virgilio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | | | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia ASST Spedali Civili of Brescia, 25123 Brescia, Italy;
| | - Fabio Ferreli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Gian Marco Frigerio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Antonio Lo Casto
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University Hospital of Palermo, 90127 Palermo, Italy;
| | - Luca Malvezzi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luigi Lorini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Medical Oncology and Hematology Unit IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ahmed E. Othman
- Department of Neuroradiology, University Medical Center Mainz, 55131 Mainz, Germany;
| | - Lorenzo Preda
- Radiology Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy;
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Paolo Bossi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Mercante
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Spriano
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luca Balzarini
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
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19
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Chen J, Yuan D, Dong R, Cai J, Ai Z, Zhou S. Artificial intelligence significantly facilitates development in the mental health of college students: a bibliometric analysis. Front Psychol 2024; 15:1375294. [PMID: 38515973 PMCID: PMC10955080 DOI: 10.3389/fpsyg.2024.1375294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2024] [Accepted: 02/26/2024] [Indexed: 03/23/2024] Open
Abstract
Objective College students are currently grappling with severe mental health challenges, and research on artificial intelligence (AI) related to college students mental health, as a crucial catalyst for promoting psychological well-being, is rapidly advancing. Employing bibliometric methods, this study aim to analyze and discuss the research on AI in college student mental health. Methods Publications pertaining to AI and college student mental health were retrieved from the Web of Science core database. The distribution of publications were analyzed to gage the predominant productivity. Data on countries, authors, journal, and keywords were analyzed using VOSViewer, exploring collaboration patterns, disciplinary composition, research hotspots and trends. Results Spanning 2003 to 2023, the study encompassed 1722 publications, revealing notable insights: (1) a gradual rise in annual publications, reaching its zenith in 2022; (2) Journal of Affective Disorders and Psychiatry Research emerged were the most productive and influential sources in this field, with significant contributions from China, the United States, and their affiliated higher education institutions; (3) the primary mental health issues were depression and anxiety, with machine learning and AI having the widest range of applications; (4) an imperative for enhanced international and interdisciplinary collaboration; (5) research hotspots exploring factors influencing college student mental health and AI applications. Conclusion This study provides a succinct yet comprehensive overview of this field, facilitating a nuanced understanding of prospective applications of AI in college student mental health. Professionals can leverage this research to discern the advantages, risks, and potential impacts of AI in this critical field.
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Affiliation(s)
- Jing Chen
- Wuhan University China Institute of Boundary and Ocean Studies, Wuhan, China
| | - Dongfeng Yuan
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Ruotong Dong
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Jingyi Cai
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
| | - Zhongzhu Ai
- Faculty of Pharmacy, Hubei University of Chinese Medicine, Wuhan, China
- Hubei Shizhen Laboratory, Wuhan, China
| | - Shanshan Zhou
- Hubei Shizhen Laboratory, Wuhan, China
- The First Clinical Medical School, Hubei University of Chinese Medicine, Wuhan, China
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20
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Nyquist ML, Fink LA, Mauldin GE, Coffman CR. Evaluation of a Novel Veterinary Dental Radiography Artificial Intelligence Software Program. J Vet Dent 2024:8987564231221071. [PMID: 38321886 DOI: 10.1177/08987564231221071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
There is a growing trend of artificial intelligence (AI) applications in veterinary medicine, with the potential to assist veterinarians in clinical decisions. A commercially available, AI-based software program (AISP) for detecting common radiographic dental pathologies in dogs and cats was assessed for agreement with two human evaluators. Furcation bone loss, periapical lucency, resorptive lesion, retained tooth root, attachment (alveolar bone) loss and tooth fracture were assessed. The AISP does not attempt to diagnose or provide treatment recommendations, nor has it been trained to identify other types of radiographic pathology. Inter-rater reliability for detecting pathologies was measured by absolute percent agreement and Gwet's agreement coefficient. There was good to excellent inter-rater reliability among all raters, suggesting the AISP performs similarly at detecting the specified pathologies compared to human evaluators. Sensitivity and specificity for the AISP were assessed using human evaluators as the reference standard. The results revealed a trend of low sensitivity and high specificity, suggesting the AISP may produce a high rate of false negatives and may not be a good tool for initial screening. However, the low rate of false positives produced by the AISP suggests it may be beneficial as a "second set of eyes" because if it detects the specific pathology, there is a high likelihood that the pathology is present. With an understanding of the AISP, as an aid and not a substitute for veterinarians, the technology may increase dental radiography utilization and diagnostic potential.
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Affiliation(s)
| | - Lisa A Fink
- Arizona Veterinary Dental Specialists, Scottsdale, AZ, USA
| | | | - Curt R Coffman
- Arizona Veterinary Dental Specialists, Scottsdale, AZ, USA
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21
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Zhou Y, Moon C, Szatkowski J, Moore D, Stevens J. Evaluating ChatGPT responses in the context of a 53-year-old male with a femoral neck fracture: a qualitative analysis. EUROPEAN JOURNAL OF ORTHOPAEDIC SURGERY & TRAUMATOLOGY : ORTHOPEDIE TRAUMATOLOGIE 2024; 34:927-955. [PMID: 37776392 PMCID: PMC10858115 DOI: 10.1007/s00590-023-03742-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 09/18/2023] [Indexed: 10/02/2023]
Abstract
PURPOSE The integration of artificial intelligence (AI) tools, such as ChatGPT, in clinical medicine and medical education has gained significant attention due to their potential to support decision-making and improve patient care. However, there is a need to evaluate the benefits and limitations of these tools in specific clinical scenarios. METHODS This study used a case study approach within the field of orthopaedic surgery. A clinical case report featuring a 53-year-old male with a femoral neck fracture was used as the basis for evaluation. ChatGPT, a large language model, was asked to respond to clinical questions related to the case. The responses generated by ChatGPT were evaluated qualitatively, considering their relevance, justification, and alignment with the responses of real clinicians. Alternative dialogue protocols were also employed to assess the impact of additional prompts and contextual information on ChatGPT responses. RESULTS ChatGPT generally provided clinically appropriate responses to the questions posed in the clinical case report. However, the level of justification and explanation varied across the generated responses. Occasionally, clinically inappropriate responses and inconsistencies were observed in the generated responses across different dialogue protocols and on separate days. CONCLUSIONS The findings of this study highlight both the potential and limitations of using ChatGPT in clinical practice. While ChatGPT demonstrated the ability to provide relevant clinical information, the lack of consistent justification and occasional clinically inappropriate responses raise concerns about its reliability. These results underscore the importance of careful consideration and validation when using AI tools in healthcare. Further research and clinician training are necessary to effectively integrate AI tools like ChatGPT, ensuring their safe and reliable use in clinical decision-making.
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Affiliation(s)
- Yushy Zhou
- Department of Surgery, The University of Melbourne, St. Vincent's Hospital Melbourne, 29 Regent Street, Clinical Sciences Block Level 2, Melbourne, VIC, 3010, Australia.
- Department of Orthopaedic Surgery, St. Vincent's Hospital, Melbourne, Australia.
| | - Charles Moon
- Department of Orthopaedic Surgery, Cedars-Sinai Medical Centre, Los Angeles, CA, USA
| | - Jan Szatkowski
- Department of Orthopaedic Surgery, Indiana University Health Methodist Hospital, Indianapolis, IN, USA
| | - Derek Moore
- Santa Barbara Orthopedic Associates, Santa Barbara, CA, USA
| | - Jarrad Stevens
- Department of Orthopaedic Surgery, St. Vincent's Hospital, Melbourne, Australia
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Adigwe OP, Onavbavba G, Sanyaolu SE. Exploring the matrix: knowledge, perceptions and prospects of artificial intelligence and machine learning in Nigerian healthcare. Front Artif Intell 2024; 6:1293297. [PMID: 38314120 PMCID: PMC10834749 DOI: 10.3389/frai.2023.1293297] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2023] [Accepted: 11/21/2023] [Indexed: 02/06/2024] Open
Abstract
Background Artificial intelligence technology can be applied in several aspects of healthcare delivery and its integration into the Nigerian healthcare value chain is expected to bring about new opportunities. This study aimed at assessing the knowledge and perception of healthcare professionals in Nigeria regarding the application of artificial intelligence and machine learning in the health sector. Methods A cross-sectional study was undertaken amongst healthcare professionals in Nigeria with the use of a questionnaire. Data were collected across the six geopolitical zones in the Country using a stratified multistage sampling method. Descriptive and inferential statistical analyses were undertaken for the data obtained. Results Female participants (55.7%) were slightly higher in proportion compared to the male respondents (44.3%). Pharmacists accounted for 27.7% of the participants, and this was closely followed by medical doctors (24.5%) and nurses (19.3%). The majority of the respondents (57.2%) reported good knowledge regarding artificial intelligence and machine learning, about a third of the participants (32.2%) were of average knowledge, and 10.6% of the sample had poor knowledge. More than half of the respondents (57.8%) disagreed with the notion that the adoption of artificial intelligence in the Nigerian healthcare sector could result in job losses. Two-thirds of the participants (66.7%) were of the view that the integration of artificial intelligence in healthcare will augment human intelligence. Three-quarters (77%) of the respondents agreed that the use of machine learning in Nigerian healthcare could facilitate efficient service delivery. Conclusion This study provides novel insights regarding healthcare professionals' knowledge and perception with respect to the application of artificial intelligence and machine learning in healthcare. The emergent findings from this study can guide government and policymakers in decision-making as regards deployment of artificial intelligence and machine learning for healthcare delivery.
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Affiliation(s)
- Obi Peter Adigwe
- National Institute for Pharmaceutical Research and Development, Abuja, Nigeria
| | - Godspower Onavbavba
- National Institute for Pharmaceutical Research and Development, Abuja, Nigeria
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Lian Y, Li X, Lan Y, Li Z, Lin X, Huang J, Zhang B, Feng Z. Bibliometric and visual analysis in the field of tea in cancer from 2013 to 2023. Front Oncol 2024; 13:1296511. [PMID: 38273848 PMCID: PMC10808711 DOI: 10.3389/fonc.2023.1296511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2023] [Accepted: 12/26/2023] [Indexed: 01/27/2024] Open
Abstract
Objective Tea has been utilized in cancer research and is progressively gaining wider recognition, with its roles in cancer prevention and treatment being increasingly affirmed. The objective of this study is to investigate the current state and research hotspots in the field of tea's involvement in cancer research from 2013 to 2023, aiming to offer reference and direction for future studies. Methods We analyzed 4,789 articles published between 2013 and 2022 from the Web of Science database using VOSviewer, R software, and CiteSpace software. Result Tea-related cancer research showed an overall upward trend, with China leading in publications, followed by the United States, India, Japan, and Italy. China also had significant international collaborations, notably with Harvard University and the Egyptian Knowledge Bank. The 'Journal of Agricultural and Food Chemistry' was the most cited journal. Key topics included 'green tea,' 'cancer,' 'in vitro,' 'oxidative stress,' and 'apoptosis.' Research focused on tea's pharmacological effects, anticancer properties, mechanisms of natural compounds (e.g., polyphenols and EGCG), antioxidant and antimicrobial properties, and molecular mechanisms in cancer treatment. Conclusion Tea's potential as an anti-cancer medication is gaining global recognition. Our study provides a comprehensive analysis of tea-related cancer research from 2013 to 2023, guiding future investigations in this field.
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Affiliation(s)
- Yuanchu Lian
- Scientific Research Center, Guilin Medical University, Guilin, China
- Lingui Clinical College, Guilin Medical University, Guilin, China
| | - Xiating Li
- Department of Pharmacy, Guangxi Academy of Medical Sciences and the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Ying Lan
- Department of Pharmacy, Guangxi Academy of Medical Sciences and the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
| | - Zonghuai Li
- Scientific Research Center, Guilin Medical University, Guilin, China
- Lingui Clinical College, Guilin Medical University, Guilin, China
| | - Xiaoxin Lin
- Scientific Research Center, Guilin Medical University, Guilin, China
- Lingui Clinical College, Guilin Medical University, Guilin, China
| | - Jing’an Huang
- Scientific Research Center, Guilin Medical University, Guilin, China
- Lingui Clinical College, Guilin Medical University, Guilin, China
| | - Bo Zhang
- Scientific Research Center, Guilin Medical University, Guilin, China
| | - Zhongwen Feng
- Department of Pharmacy, Guangxi Academy of Medical Sciences and the People’s Hospital of Guangxi Zhuang Autonomous Region, Nanning, China
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Hussain W, Mabrok M, Gao H, Rabhi FA, Rashed EA. Revolutionising healthcare with artificial intelligence: A bibliometric analysis of 40 years of progress in health systems. Digit Health 2024; 10:20552076241258757. [PMID: 38817839 PMCID: PMC11138196 DOI: 10.1177/20552076241258757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Accepted: 05/14/2024] [Indexed: 06/01/2024] Open
Abstract
The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include 'ML', 'Deep Learning', and 'Artificial Intelligence'.
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Affiliation(s)
- Walayat Hussain
- Peter Faber Business School, Australian Catholic University, North Sydney, Australia
| | - Mohamed Mabrok
- Department of Mathematics and Statistics, Qatar University, Doha, Qatar
| | - Honghao Gao
- School of Computer Engineering and Science, Shanghai University, Shanghai, China
| | - Fethi A. Rabhi
- School of Computer Science and Engineering, University of New South Wales (UNSW), Sydney, Australia
| | - Essam A. Rashed
- Graduate School of Information Science, University of Hyogo, Kobe, Japan
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Bottomley D, Thaldar D. Liability for harm caused by AI in healthcare: an overview of the core legal concepts. Front Pharmacol 2023; 14:1297353. [PMID: 38161692 PMCID: PMC10755877 DOI: 10.3389/fphar.2023.1297353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/19/2023] [Accepted: 11/27/2023] [Indexed: 01/03/2024] Open
Abstract
The integration of artificial intelligence (AI) into healthcare in Africa presents transformative opportunities but also raises profound legal challenges, especially concerning liability. As AI becomes more autonomous, determining who or what is responsible when things go wrong becomes ambiguous. This article aims to review the legal concepts relevant to the issue of liability for harm caused by AI in healthcare. While some suggest attributing legal personhood to AI as a potential solution, the feasibility of this remains controversial. The principal-agent relationship, where the physician is held responsible for AI decisions, risks reducing the adoption of AI tools due to potential liabilities. Similarly, using product law to establish liability is problematic because of the dynamic learning nature of AI, which deviates from static products. This fluidity complicates traditional definitions of product defects and, by extension, where responsibility lies. Exploring alternatives, risk-based determinations of liability, which focus on potential hazards rather than on specific fault assignments, emerges as a potential pathway. However, these, too, present challenges in assigning accountability. Strict liability has been proposed as another avenue. It can simplify the compensation process for victims by focusing on the harm rather than on the fault. Yet, concerns arise over the economic impact on stakeholders, the potential for unjust reputational damage, and the feasibility of a global application. Instead of approaches based on liability, reconciliation holds much promise to facilitate regulatory sandboxes. In conclusion, while the integration of AI systems into healthcare holds vast potential, it necessitates a re-evaluation of our legal frameworks. The central challenge is how to adapt traditional concepts of liability to the novel and unpredictable nature of AI-or to move away from liability towards reconciliation. Future discussions and research must navigate these complex waters and seek solutions that ensure both progress and protection.
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Affiliation(s)
| | - Donrich Thaldar
- School of Law, University of KwaZulu-Natal, Durban, South Africa
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26
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Das A, Dhillon P. Application of machine learning in measurement of ageing and geriatric diseases: a systematic review. BMC Geriatr 2023; 23:841. [PMID: 38087195 PMCID: PMC10717316 DOI: 10.1186/s12877-023-04477-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 11/10/2023] [Indexed: 12/18/2023] Open
Abstract
BACKGROUND As the ageing population continues to grow in many countries, the prevalence of geriatric diseases is on the rise. In response, healthcare providers are exploring novel methods to enhance the quality of life for the elderly. Over the last decade, there has been a remarkable surge in the use of machine learning in geriatric diseases and care. Machine learning has emerged as a promising tool for the diagnosis, treatment, and management of these conditions. Hence, our study aims to find out the present state of research in geriatrics and the application of machine learning methods in this area. METHODS This systematic review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines and focused on healthy ageing in individuals aged 45 and above, with a specific emphasis on the diseases that commonly occur during this process. The study mainly focused on three areas, that are machine learning, the geriatric population, and diseases. Peer-reviewed articles were searched in the PubMed and Scopus databases with inclusion criteria of population above 45 years, must have used machine learning methods, and availability of full text. To assess the quality of the studies, Joanna Briggs Institute's (JBI) critical appraisal tool was used. RESULTS A total of 70 papers were selected from the 120 identified papers after going through title screening, abstract screening, and reference search. Limited research is available on predicting biological or brain age using deep learning and different supervised machine learning methods. Neurodegenerative disorders were found to be the most researched disease, in which Alzheimer's disease was focused the most. Among non-communicable diseases, diabetes mellitus, hypertension, cancer, kidney diseases, and cardiovascular diseases were included, and other rare diseases like oral health-related diseases and bone diseases were also explored in some papers. In terms of the application of machine learning, risk prediction was the most common approach. Half of the studies have used supervised machine learning algorithms, among which logistic regression, random forest, XG Boost were frequently used methods. These machine learning methods were applied to a variety of datasets including population-based surveys, hospital records, and digitally traced data. CONCLUSION The review identified a wide range of studies that employed machine learning algorithms to analyse various diseases and datasets. While the application of machine learning in geriatrics and care has been well-explored, there is still room for future development, particularly in validating models across diverse populations and utilizing personalized digital datasets for customized patient-centric care in older populations. Further, we suggest a scope of Machine Learning in generating comparable ageing indices such as successful ageing index.
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Affiliation(s)
- Ayushi Das
- International Institute for Population Sciences, Deonar, Mumbai, 400088, India
| | - Preeti Dhillon
- Department of Survey Research and Data Analytics, International Institute for Population Sciences, Deonar, Mumbai, 400088, India.
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Chatterjee S, Bhattacharya M, Pal S, Lee SS, Chakraborty C. ChatGPT and large language models in orthopedics: from education and surgery to research. J Exp Orthop 2023; 10:128. [PMID: 38038796 PMCID: PMC10692045 DOI: 10.1186/s40634-023-00700-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Accepted: 11/16/2023] [Indexed: 12/02/2023] Open
Abstract
ChatGPT has quickly popularized since its release in November 2022. Currently, large language models (LLMs) and ChatGPT have been applied in various domains of medical science, including in cardiology, nephrology, orthopedics, ophthalmology, gastroenterology, and radiology. Researchers are exploring the potential of LLMs and ChatGPT for clinicians and surgeons in every domain. This study discusses how ChatGPT can help orthopedic clinicians and surgeons perform various medical tasks. LLMs and ChatGPT can help the patient community by providing suggestions and diagnostic guidelines. In this study, the use of LLMs and ChatGPT to enhance and expand the field of orthopedics, including orthopedic education, surgery, and research, is explored. Present LLMs have several shortcomings, which are discussed herein. However, next-generation and future domain-specific LLMs are expected to be more potent and transform patients' quality of life.
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Affiliation(s)
- Srijan Chatterjee
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-Si, 24252, Gangwon-Do, Republic of Korea
| | - Manojit Bhattacharya
- Department of Zoology, Fakir Mohan University, Vyasa Vihar, Balasore, 756020, Odisha, India
| | - Soumen Pal
- School of Mechanical Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India
| | - Sang-Soo Lee
- Institute for Skeletal Aging & Orthopaedic Surgery, Hallym University-Chuncheon Sacred Heart Hospital, Chuncheon-Si, 24252, Gangwon-Do, Republic of Korea.
| | - Chiranjib Chakraborty
- Department of Biotechnology, School of Life Science and Biotechnology, Adamas University, Kolkata, West Bengal, 700126, India.
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Haze T, Kawano R, Takase H, Suzuki S, Hirawa N, Tamura K. Influence on the accuracy in ChatGPT: Differences in the amount of information per medical field. Int J Med Inform 2023; 180:105283. [PMID: 37931432 DOI: 10.1016/j.ijmedinf.2023.105283] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 11/08/2023]
Abstract
OBJECTIVES Although ChatGPT was not developed for medical use, there is growing interest in its use in medical fields. Understanding its capabilities and precautions for its use in the medical field is an urgent matter. We hypothesized that differences in the amounts of information published in different medical fields would be proportionate to the amounts of training ChatGPT receives in those fields, and hence its accuracy in providing answers. STUDY DESIGN A non-clinical experimental study. METHODS We administered the Japanese National Medical Examination to GPT-3.5 and GPT-4 to examine the rates of accuracy and consistency in their responses. We counted the total number of documents in the Web of Science Core Collection per medical field and assessed the relationship with ChatGPT's accuracy. We also performed multivariate-adjusted models to investigate the risk factors for incorrect answers. RESULTS For GPT-4, we confirmed an accuracy rate of 81.0 % and a consistency rate of 88.8 % on the exam; both showed improvement compared to those for GPT-3.5. A positive correlation was observed between the accuracy rate and consistency rate (R = 0.51, P < 0.001). The number of documents per medical field was significantly correlated with the accuracy rate in that medical field (R = 0.44, P < 0.05), with relatively few publications being an independent risk factor for incorrect answers. CONCLUSIONS Checking consistency may help identify incorrect answers when using ChatGPT. Users should be aware that the accuracy of the answers by ChatGPT may decrease when it is asked about topics with limited published information, such as new drugs and diseases.
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Affiliation(s)
- Tatsuya Haze
- Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan; Department of Nephrology and Hypertension, Yokohama City University Medical Center, Yokohama, Japan; YCU Center for Novel and Exploratory Clinical Trials (Y-NEXT), Yokohama City University Hospital, Yokohama, Japan.
| | - Rina Kawano
- Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan; Department of Nephrology and Hypertension, Yokohama City University Medical Center, Yokohama, Japan
| | - Hajime Takase
- YCU Center for Novel and Exploratory Clinical Trials (Y-NEXT), Yokohama City University Hospital, Yokohama, Japan; Department of Neurosurgery, Yokohama City University Graduate School of Medicine, Yokohama, Japan
| | - Shota Suzuki
- Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan; Department of Nephrology and Hypertension, Yokohama City University Medical Center, Yokohama, Japan
| | - Nobuhito Hirawa
- Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan; Department of Nephrology and Hypertension, Yokohama City University Medical Center, Yokohama, Japan; Clinical Education and Training Center, Yokohama City University Medical Center, Yokohama, Japan
| | - Kouichi Tamura
- Department of Medical Science and Cardiorenal Medicine, Yokohama City University Graduate School of Medicine, Yokohama, Japan
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Kocak B, Baessler B, Cuocolo R, Mercaldo N, Pinto Dos Santos D. Trends and statistics of artificial intelligence and radiomics research in Radiology, Nuclear Medicine, and Medical Imaging: bibliometric analysis. Eur Radiol 2023; 33:7542-7555. [PMID: 37314469 DOI: 10.1007/s00330-023-09772-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 04/02/2023] [Accepted: 04/14/2023] [Indexed: 06/15/2023]
Abstract
OBJECTIVE To conduct a comprehensive bibliometric analysis of artificial intelligence (AI) and its subfields as well as radiomics in Radiology, Nuclear Medicine, and Medical Imaging (RNMMI). METHODS Web of Science was queried for relevant publications in RNMMI and medicine along with their associated data from 2000 to 2021. Bibliometric techniques utilised were co-occurrence, co-authorship, citation burst, and thematic evolution analyses. Growth rate and doubling time were also estimated using log-linear regression analyses. RESULTS According to the number of publications, RNMMI (11,209; 19.8%) was the most prominent category in medicine (56,734). USA (44.6%) and China (23.1%) were the two most productive and collaborative countries. USA and Germany experienced the strongest citation bursts. Thematic evolution has recently exhibited a significant shift toward deep learning. In all analyses, the annual number of publications and citations demonstrated exponential growth, with deep learning-based publications exhibiting the most prominent growth pattern. Estimated continuous growth rate, annual growth rate, and doubling time of the AI and machine learning publications in RNMMI were 26.1% (95% confidence interval [CI], 12.0-40.2%), 29.8% (95% CI, 12.7-49.5%), and 2.7 years (95% CI, 1.7-5.8), respectively. In the sensitivity analysis using data from the last 5 and 10 years, these estimates ranged from 47.6 to 51.1%, 61.0 to 66.7%, and 1.4 to 1.5 years. CONCLUSION This study provides an overview of AI and radiomics research conducted mainly in RNMMI. These results may assist researchers, practitioners, policymakers, and organisations in gaining a better understanding of both the evolution of these fields and the importance of supporting (e.g., financial) these research activities. KEY POINTS • In terms of the number of publications on AI and ML, Radiology, Nuclear Medicine, and Medical Imaging was the most prominent category compared to the other categories related to medicine (e.g., Health Policy & Services, Surgery). • All evaluated analyses (i.e., AI, its subfields, and radiomics), based on the annual number of publications and citations, demonstrated exponential growth, with decreasing doubling time, which indicates increasing interest from researchers, journals, and, in turn, the medical imaging community. • The most prominent growth pattern was observed in deep learning-based publications. However, the further thematic analysis demonstrated that deep learning has been underdeveloped but highly relevant to the medical imaging community.
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Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, Istanbul, 34480, Turkey.
| | - Bettina Baessler
- Department of Diagnostic and Interventional Radiology, University Hospital Würzburg, Würzburg, Germany
| | - Renato Cuocolo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Nathaniel Mercaldo
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Daniel Pinto Dos Santos
- Department of Radiology, University Hospital of Cologne, Cologne, Germany
- Institute for Diagnostic and Interventional Radiology, Goethe-University Frankfurt am Main, Frankfurt, Germany
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Samuel G. UK health researchers' considerations of the environmental impacts of their data-intensive practices and its relevance to health inequities. BMC Med Ethics 2023; 24:90. [PMID: 37891541 PMCID: PMC10612270 DOI: 10.1186/s12910-023-00973-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2023] [Accepted: 10/17/2023] [Indexed: 10/29/2023] Open
Abstract
BACKGROUND The health sector aims to improve health outcomes and access to healthcare. At the same time, the sector relies on unsustainable environmental practices that are increasingly recognised to be catastrophic threats to human health and health inequities. As such, a moral imperative exists for the sector to address these practices. While strides are currently underway to mitigate the environmental impacts of healthcare, less is known about how health researchers are addressing these issues, if at all. METHODS This paper uses an interview methodology to explore the attitudes of UK health researchers using data-intensive methodologies about the adverse environmental impacts of their practices, and how they view the importance of these considerations within wider health goals. RESULTS Interviews with 26 researchers showed that participants wanted to address the environmental and related health harms associated with their research and they reflected on how they could do so in alignment with their own research goals. However, when tensions emerged, their own research was prioritised. This was related to their own desires as researchers and driven by the broader socio-political context of their research endeavours. CONCLUSION To help mitigate the environmental and health harms associated with data-intensive health research, the socio-political context of research culture must be addressed.
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Affiliation(s)
- Gabrielle Samuel
- Department of Global Health and Social Medicine, King's College London, London, Strand, UK.
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Shiferaw KB, Roloff M, Waltemath D, Zeleke AA. Guidelines and Standard Frameworks for AI in Medicine: Protocol for a Systematic Literature Review. JMIR Res Protoc 2023; 12:e47105. [PMID: 37878365 PMCID: PMC10632920 DOI: 10.2196/47105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2023] [Revised: 06/09/2023] [Accepted: 06/20/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Applications of artificial intelligence (AI) are pervasive in modern biomedical science. In fact, research results suggesting algorithms and AI models for different target diseases and conditions are continuously increasing. While this situation undoubtedly improves the outcome of AI models, health care providers are increasingly unsure which AI model to use due to multiple alternatives for a specific target and the "black box" nature of AI. Moreover, the fact that studies rarely use guidelines in developing and reporting AI models poses additional challenges in trusting and adapting models for practical implementation. OBJECTIVE This review protocol describes the planned steps and methods for a review of the synthesized evidence regarding the quality of available guidelines and frameworks to facilitate AI applications in medicine. METHODS We will commence a systematic literature search using medical subject headings terms for medicine, guidelines, and machine learning (ML). All available guidelines, standard frameworks, best practices, checklists, and recommendations will be included, irrespective of the study design. The search will be conducted on web-based repositories such as PubMed, Web of Science, and the EQUATOR (Enhancing the Quality and Transparency of Health Research) network. After removing duplicate results, a preliminary scan for titles will be done by 2 reviewers. After the first scan, the reviewers will rescan the selected literature for abstract review, and any incongruities about whether to include the article for full-text review or not will be resolved by the third and fourth reviewer based on the predefined criteria. A Google Scholar (Google LLC) search will also be performed to identify gray literature. The quality of identified guidelines will be evaluated using the Appraisal of Guidelines, Research, and Evaluation (AGREE II) tool. A descriptive summary and narrative synthesis will be carried out, and the details of critical appraisal and subgroup synthesis findings will be presented. RESULTS The results will be reported using the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) reporting guidelines. Data analysis is currently underway, and we anticipate finalizing the review by November 2023. CONCLUSIONS Guidelines and recommended frameworks for developing, reporting, and implementing AI studies have been developed by different experts to facilitate the reliable assessment of validity and consistent interpretation of ML models for medical applications. We postulate that a guideline supports the assessment of an ML model only if the quality and reliability of the guideline are high. Assessing the quality and aspects of available guidelines, recommendations, checklists, and frameworks-as will be done in the proposed review-will provide comprehensive insights into current gaps and help to formulate future research directions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/47105.
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Affiliation(s)
- Kirubel Biruk Shiferaw
- Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Moritz Roloff
- Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Dagmar Waltemath
- Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
| | - Atinkut Alamirrew Zeleke
- Department of Medical Informatics, Institute for Community Medicine, University Medicine Greifswald, Greifswald, Germany
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Ogilvie T, Kastelic J. In the 21st century, "AI" has a new meaning for veterinarians! THE CANADIAN VETERINARY JOURNAL = LA REVUE VETERINAIRE CANADIENNE 2023; 64:897-898. [PMID: 37780470 PMCID: PMC10506353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 10/03/2023]
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Kocak B, Yardimci AH, Yuzkan S, Keles A, Altun O, Bulut E, Bayrak ON, Okumus AA. Transparency in Artificial Intelligence Research: a Systematic Review of Availability Items Related to Open Science in Radiology and Nuclear Medicine. Acad Radiol 2023; 30:2254-2266. [PMID: 36526532 DOI: 10.1016/j.acra.2022.11.030] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Revised: 11/21/2022] [Accepted: 11/22/2022] [Indexed: 12/15/2022]
Abstract
RATIONALE AND OBJECTIVES Reproducibility of artificial intelligence (AI) research has become a growing concern. One of the fundamental reasons is the lack of transparency in data, code, and model. In this work, we aimed to systematically review the radiology and nuclear medicine papers on AI in terms of transparency and open science. MATERIALS AND METHODS A systematic literature search was performed in PubMed to identify original research studies on AI. The search was restricted to studies published in Q1 and Q2 journals that are also indexed on the Web of Science. A random sampling of the literature was performed. Besides six baseline study characteristics, a total of five availability items were evaluated. Two groups of independent readers including eight readers participated in the study. Inter-rater agreement was analyzed. Disagreements were resolved with consensus. RESULTS Following eligibility criteria, we included a final set of 194 papers. The raw data was available in about one-fifth of the papers (34/194; 18%). However, the authors made their private data available only in one paper (1/161; 1%). About one-tenth of the papers made their pre-modeling (25/194; 13%), modeling (28/194; 14%), or post-modeling files (15/194; 8%) available. Most of the papers (189/194; 97%) did not attempt to create a ready-to-use system for real-world usage. Data origin, use of deep learning, and external validation had statistically significantly different distributions. The use of private data alone was negatively associated with the availability of at least one item (p<0.001). CONCLUSION Overall rates of availability for items were poor, leaving room for substantial improvement.
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Affiliation(s)
- Burak Kocak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey.
| | - Aytul Hande Yardimci
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Sabahattin Yuzkan
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Ali Keles
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Omer Altun
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Elif Bulut
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Osman Nuri Bayrak
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
| | - Ahmet Arda Okumus
- Department of Radiology, University of Health Sciences, Basaksehir Cam and Sakura City Hospital, Basaksehir, 34480, Istanbul, Turkey
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Frascarelli C, Bonizzi G, Musico CR, Mane E, Cassi C, Guerini Rocco E, Farina A, Scarpa A, Lawlor R, Reggiani Bonetti L, Caramaschi S, Eccher A, Marletta S, Fusco N. Revolutionizing Cancer Research: The Impact of Artificial Intelligence in Digital Biobanking. J Pers Med 2023; 13:1390. [PMID: 37763157 PMCID: PMC10532470 DOI: 10.3390/jpm13091390] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2023] [Revised: 09/05/2023] [Accepted: 09/12/2023] [Indexed: 09/29/2023] Open
Abstract
BACKGROUND Biobanks are vital research infrastructures aiming to collect, process, store, and distribute biological specimens along with associated data in an organized and governed manner. Exploiting diverse datasets produced by the biobanks and the downstream research from various sources and integrating bioinformatics and "omics" data has proven instrumental in advancing research such as cancer research. Biobanks offer different types of biological samples matched with rich datasets comprising clinicopathologic information. As digital pathology and artificial intelligence (AI) have entered the precision medicine arena, biobanks are progressively transitioning from mere biorepositories to integrated computational databanks. Consequently, the application of AI and machine learning on these biobank datasets holds huge potential to profoundly impact cancer research. METHODS In this paper, we explore how AI and machine learning can respond to the digital evolution of biobanks with flexibility, solutions, and effective services. We look at the different data that ranges from specimen-related data, including digital images, patient health records and downstream genetic/genomic data and resulting "Big Data" and the analytic approaches used for analysis. RESULTS These cutting-edge technologies can address the challenges faced by translational and clinical research, enhancing their capabilities in data management, analysis, and interpretation. By leveraging AI, biobanks can unlock valuable insights from their vast repositories, enabling the identification of novel biomarkers, prediction of treatment responses, and ultimately facilitating the development of personalized cancer therapies. CONCLUSIONS The integration of biobanking with AI has the potential not only to expand the current understanding of cancer biology but also to pave the way for more precise, patient-centric healthcare strategies.
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Affiliation(s)
- Chiara Frascarelli
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Giuseppina Bonizzi
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (G.B.); (C.R.M.); (C.C.)
| | - Camilla Rosella Musico
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (G.B.); (C.R.M.); (C.C.)
| | - Eltjona Mane
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
| | - Cristina Cassi
- Biobank for Translational and Digital Medicine, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (G.B.); (C.R.M.); (C.C.)
| | - Elena Guerini Rocco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
| | - Annarosa Farina
- Central Information Systems and Technology Directorate, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy;
| | - Aldo Scarpa
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy; (A.S.); (S.M.)
| | - Rita Lawlor
- ARC-Net Research Centre and Department of Diagnostics and Public Health, University of Verona, 37134 Verona, Italy;
| | - Luca Reggiani Bonetti
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41121 Modena, Italy; (L.R.B.); (S.C.)
| | - Stefania Caramaschi
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41121 Modena, Italy; (L.R.B.); (S.C.)
| | - Albino Eccher
- Section of Pathology, Department of Medical and Surgical Sciences for Children and Adults, University of Modena and Reggio Emilia, University Hospital of Modena, 41121 Modena, Italy; (L.R.B.); (S.C.)
| | - Stefano Marletta
- Department of Diagnostics and Public Health, Section of Pathology, University of Verona, 37134 Verona, Italy; (A.S.); (S.M.)
- Division of Pathology, Humanitas Cancer Center, 95045 Catania, Italy
| | - Nicola Fusco
- Division of Pathology, IEO, European Institute of Oncology IRCCS, 20139 Milan, Italy; (C.F.); (E.M.); (E.G.R.); (N.F.)
- Department of Oncology and Hemato-Oncology, University of Milan, 20122 Milan, Italy
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Malinverno L, Barros V, Ghisoni F, Visonà G, Kern R, Nickel PJ, Ventura BE, Šimić I, Stryeck S, Manni F, Ferri C, Jean-Quartier C, Genga L, Schweikert G, Lovrić M, Rosen-Zvi M. A historical perspective of biomedical explainable AI research. PATTERNS (NEW YORK, N.Y.) 2023; 4:100830. [PMID: 37720333 PMCID: PMC10500028 DOI: 10.1016/j.patter.2023.100830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 09/19/2023]
Abstract
The black-box nature of most artificial intelligence (AI) models encourages the development of explainability methods to engender trust into the AI decision-making process. Such methods can be broadly categorized into two main types: post hoc explanations and inherently interpretable algorithms. We aimed at analyzing the possible associations between COVID-19 and the push of explainable AI (XAI) to the forefront of biomedical research. We automatically extracted from the PubMed database biomedical XAI studies related to concepts of causality or explainability and manually labeled 1,603 papers with respect to XAI categories. To compare the trends pre- and post-COVID-19, we fit a change point detection model and evaluated significant changes in publication rates. We show that the advent of COVID-19 in the beginning of 2020 could be the driving factor behind an increased focus concerning XAI, playing a crucial role in accelerating an already evolving trend. Finally, we present a discussion with future societal use and impact of XAI technologies and potential future directions for those who pursue fostering clinical trust with interpretable machine learning models.
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Affiliation(s)
| | - Vesna Barros
- AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel
- The Hebrew University of Jerusalem, Ein Kerem Campus, 9112102, Jerusalem, Israel
| | | | - Giovanni Visonà
- Empirical Inference, Max-Planck Institute for Intelligent Systems, 72076 Tübingen, Germany
| | - Roman Kern
- Institute of Interactive Systems and Data Science, Graz University of Technology, Sandgasse 36/III, 8010 Graz, Austria
- Know-Center GmbH, Sandgasse 36/4A 8010, Graz, Austria
| | - Philip J. Nickel
- Eindhoven University of Technology, 5135600 MB Eindhoven, The Netherlands
| | | | - Ilija Šimić
- Know-Center GmbH, Sandgasse 36/4A 8010, Graz, Austria
| | - Sarah Stryeck
- Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 138010 Graz, Austria
| | | | - Cesar Ferri
- VRAIN, Universitat Politècnica de València, Camino de Vera, s/n 46022 Valencia, Spain
| | - Claire Jean-Quartier
- Research Data Management, Graz University of Technology, Brockmanngasse 84, 8010 Graz, Austria
| | - Laura Genga
- Eindhoven University of Technology, 5135600 MB Eindhoven, The Netherlands
| | - Gabriele Schweikert
- School of Life Sciences, University of Dundee, Dow Street, Dundee DD1 5EH, UK
| | - Mario Lovrić
- Know-Center GmbH, Sandgasse 36/4A 8010, Graz, Austria
- Centre for Applied Bioanthropology, Institute for Anthropological Research, 10000 Zagreb, Croatia
| | - Michal Rosen-Zvi
- AI for Accelerated Healthcare & Life Sciences Discovery, IBM R&D Laboratories, University of Haifa Campus, Mount Carmel, Haifa 3498825, Israel
- The Hebrew University of Jerusalem, Ein Kerem Campus, 9112102, Jerusalem, Israel
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Lam A, Squires E, Tan S, Swen NJ, Barilla A, Kovoor J, Gupta A, Bacchi S, Khurana S. Artificial intelligence for predicting acute appendicitis: a systematic review. ANZ J Surg 2023; 93:2070-2078. [PMID: 37458222 DOI: 10.1111/ans.18610] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/06/2023] [Indexed: 09/16/2023]
Abstract
BACKGROUND Paediatric appendicitis may be challenging to diagnose, and outcomes difficult to predict. While diagnostic and prognostic scores exist, artificial intelligence (AI) may be able to assist with these tasks. METHOD A systematic review was conducted aiming to evaluate the currently available evidence regarding the use of AI in the diagnosis and prognostication of paediatric appendicitis. In accordance with the PRISMA guidelines, the databases PubMed, EMBASE, and Cochrane Library were searched. This review was prospectively registered on PROSPERO. RESULTS Ten studies met inclusion criteria. All studies described the derivation and validation of AI models, and none described evaluation of the implementation of these models. Commonly used input parameters included varying combinations of demographic, clinical, laboratory, and imaging characteristics. While multiple studies used histopathological examination as the ground truth for a diagnosis of appendicitis, less robust techniques, such as the use of ICD10 codes, were also employed. Commonly used algorithms have included random forest models and artificial neural networks. High levels of model performance have been described for diagnosis of appendicitis and, to a lesser extent, subtypes of appendicitis (such as complicated versus uncomplicated appendicitis). Most studies did not provide all measures of model performance required to assess clinical usability. CONCLUSIONS The available evidence suggests the creation of prediction models for diagnosis and classification of appendicitis using AI techniques, is being increasingly explored. However, further implementation studies are required to demonstrate benefit in system or patient-centred outcomes with model deployment and to progress these models to the stage of clinical usability.
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Affiliation(s)
- Antoinette Lam
- University of Adelaide, Adelaide, South Australia, Australia
| | - Emily Squires
- Flinders University, Adelaide, South Australia, Australia
| | - Sheryn Tan
- University of Adelaide, Adelaide, South Australia, Australia
| | - Ng Jeng Swen
- University of Adelaide, Adelaide, South Australia, Australia
| | | | - Joshua Kovoor
- University of Adelaide, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Aashray Gupta
- University of Adelaide, Adelaide, South Australia, Australia
- Women's and Children's Hospital, Adelaide, South Australia, Australia
| | - Stephen Bacchi
- University of Adelaide, Adelaide, South Australia, Australia
- Flinders University, Adelaide, South Australia, Australia
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Sanjeev Khurana
- University of Adelaide, Adelaide, South Australia, Australia
- Women's and Children's Hospital, Adelaide, South Australia, Australia
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Rokhshad R, Ducret M, Chaurasia A, Karteva T, Radenkovic M, Roganovic J, Hamdan M, Mohammad-Rahimi H, Krois J, Lahoud P, Schwendicke F. Ethical considerations on artificial intelligence in dentistry: A framework and checklist. J Dent 2023; 135:104593. [PMID: 37355089 DOI: 10.1016/j.jdent.2023.104593] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 06/13/2023] [Accepted: 06/14/2023] [Indexed: 06/26/2023] Open
Abstract
OBJECTIVE Artificial Intelligence (AI) refers to the ability of machines to perform cognitive and intellectual human tasks. In dentistry, AI offers the potential to enhance diagnostic accuracy, improve patient outcomes and streamline workflows. The present study provides a framework and a checklist to evaluate AI applications in dentistry from this perspective. METHODS Lending from existing guidance documents, an initial draft of the checklist and an explanatory paper were derived and discussed among the groups members. RESULTS The checklist was consented to in an anonymous voting process by 29 Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health's members. Overall, 11 principles were identified (diversity, transparency, wellness, privacy protection, solidarity, equity, prudence, law and governance, sustainable development, accountability, and responsibility, respect of autonomy, decision-making). CONCLUSIONS Providers, patients, researchers, industry, and other stakeholders should consider these principles when developing, implementing, or receiving AI applications in dentistry. CLINICAL SIGNIFICANCE While AI has become increasingly commonplace in dentistry, there are ethical concerns around its usage, and users (providers, patients, and other stakeholders), as well as the industry should consider these when developing, implementing, or receiving AI applications based on comprehensive framework to address the associated ethical challenges.
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Affiliation(s)
- Rata Rokhshad
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany.
| | - Maxime Ducret
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Faculty of Odontology, University Claude Bernard Lyon Il, University of Lyon, Lyon, France
| | - Akhilanand Chaurasia
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Medicine and Radiology, King George's Medical University, Lucknow, India; Faculty of Dentistry, University of Puthisashtra, Combodia
| | - Teodora Karteva
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Operative Dentistry and Endodontics, Faculty of Dental Medicine, Medical University Plovdiv, Bulgaria
| | - Miroslav Radenkovic
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Pharmacology, Clinical Pharmacology and Toxicology, Faculty of Medicine, University of Belgrade, Serbia
| | - Jelena Roganovic
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Pharmacology in Dentistry, School of Dental medicine, University of Belgrade, Serbia
| | - Manal Hamdan
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; General Dental Sciences Department, Marquette University School of Dentistry, USA
| | - Hossein Mohammad-Rahimi
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany
| | - Joachim Krois
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Pierre Lahoud
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany; Department of Oral and MaxilloFacial Surgery & Imaging and Pathology- OMFS-IMPATH Research Group, KU Leuven, Belgium; Division of Periodontology and Oral Microbiology, Department of Oral Health Sciences, KU Leuven, Belgium
| | - Falk Schwendicke
- Topic Group Dental Diagnostics and Digital Dentistry, ITU/WHO Focus Group AI on Health, Berlin, Germany; Department of Oral Diagnostics, Digital Health and Health Services Research, Charité - Universitätsmedizin Berlin, Berlin, Germany
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Wu P, Cao B, Liang Z, Wu M. The advantages of artificial intelligence-based gait assessment in detecting, predicting, and managing Parkinson's disease. Front Aging Neurosci 2023; 15:1191378. [PMID: 37502426 PMCID: PMC10368956 DOI: 10.3389/fnagi.2023.1191378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 06/05/2023] [Indexed: 07/29/2023] Open
Abstract
Background Parkinson's disease is a neurological disorder that can cause gait disturbance, leading to mobility issues and falls. Early diagnosis and prediction of freeze episodes are essential for mitigating symptoms and monitoring the disease. Objective This review aims to evaluate the use of artificial intelligence (AI)-based gait evaluation in diagnosing and managing Parkinson's disease, and to explore the potential benefits of this technology for clinical decision-making and treatment support. Methods A thorough review of published literature was conducted to identify studies, articles, and research related to AI-based gait evaluation in Parkinson's disease. Results AI-based gait evaluation has shown promise in preventing freeze episodes, improving diagnosis, and increasing motor independence in patients with Parkinson's disease. Its advantages include higher diagnostic accuracy, continuous monitoring, and personalized therapeutic interventions. Conclusion AI-based gait evaluation systems hold great promise for managing Parkinson's disease and improving patient outcomes. They offer the potential to transform clinical decision-making and inform personalized therapies, but further research is needed to determine their effectiveness and refine their use.
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Affiliation(s)
- Peng Wu
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Biwei Cao
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
| | - Zhendong Liang
- College of Acupuncture and Orthopedics, Hubei University of Chinese Medicine, Wuhan, Hubei, China
| | - Miao Wu
- Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China
- Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, Hubei, China
- Hubei Academy of Traditional Chinese Medicine, Wuhan, Hubei, China
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Nguyen TT, Nguyen HT, Do HP, Ho CSH, Ho RCM. Characterizing the Development of Research Landscapes in Substance Use and HIV/AIDS During 1990 to 2021. Subst Abuse 2023; 17:11782218231177515. [PMID: 37304210 PMCID: PMC10251474 DOI: 10.1177/11782218231177515] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 05/07/2023] [Indexed: 06/13/2023]
Abstract
Mitigating the impacts of Human Immunodeficiency Virus/Acquired Immune Deficiency Syndrome (HIV/AIDS) and substance use requires comprehensive and systematic thinking in designing interventions and developing policies. This study describes the growth of research publications from 1991 to 2021 in the Web of Science database and points out current research landscapes in the fields of HIV/AIDS and substance use. Latent Dirichlet Allocation was used for classifying 21 359 papers into corresponding topics. The most common topics were HIV transmission, HIV infection, quality of life and mental health of substance users, and the biomedical effect of substance use. Emerging research landscapes include vulnerabilities of people who inject drugs to HIV transmission and related health problems. This study found a lack of research on health services, interdisciplinary and inter-sectoral in combination with clinical evaluation and treatment services. Future investment and implementation of HIV/AIDS and substance use programs should focus on research of health services and clinical evaluation, especially context-specific interventions.
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Affiliation(s)
- Tham Thi Nguyen
- Institute for Global Health Innovations, Duy Tan University, Da Nang, Vietnam
- Faculty of Medicine, Duy Tan University, Da Nang, Vietnam
| | - Hien Thu Nguyen
- Institute for Global Health Innovations, Duy Tan University, Da Nang, Vietnam
- Faculty of Medicine, Duy Tan University, Da Nang, Vietnam
| | - Huyen Phuc Do
- Institute of Health Economics and Technology, Hanoi, Vietnam
| | - Cyrus SH Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Roger CM Ho
- Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Institute for Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore
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Al Kuwaiti A, Nazer K, Al-Reedy A, Al-Shehri S, Al-Muhanna A, Subbarayalu AV, Al Muhanna D, Al-Muhanna FA. A Review of the Role of Artificial Intelligence in Healthcare. J Pers Med 2023; 13:951. [PMID: 37373940 PMCID: PMC10301994 DOI: 10.3390/jpm13060951] [Citation(s) in RCA: 50] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 05/11/2023] [Accepted: 05/12/2023] [Indexed: 06/29/2023] Open
Abstract
Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of AI in healthcare and focuses on the following key aspects: (i) medical imaging and diagnostics, (ii) virtual patient care, (iii) medical research and drug discovery, (iv) patient engagement and compliance, (v) rehabilitation, and (vi) other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using AI-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs' belief in enhancing acceptance and boosting significant health consequences. Effective governance is a prerequisite to precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs.
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Affiliation(s)
- Ahmed Al Kuwaiti
- Department of Dental Education, College of Dentistry, Deanship of Quality and Academic Accreditation, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Khalid Nazer
- Department of Information and Technology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
- Health Information Department, King Fahad hospital of the University, Al-Khobar 31952, Saudi Arabia
| | - Abdullah Al-Reedy
- Department of Information and Technology, Family and Community Medicine Department, Family and Community Medicine Centre, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Shaher Al-Shehri
- Faculty of Medicine, Family and Community Medicine Department, Family and Community Medicine Centre, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Afnan Al-Muhanna
- Breast Imaging Division, Department of Radiology, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
- Radiology Department, King Fahad hospital of the University, Al-Khobar 31952, Saudi Arabia
| | - Arun Vijay Subbarayalu
- Quality Studies and Research Unit, Vice Deanship of Quality, Deanship of Quality and Academic Accreditation, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Dhoha Al Muhanna
- NDirectorate of Quality and Patient Safety, Family and Community Medicine Center, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Fahad A. Al-Muhanna
- Nephrology Division, Department of Internal Medicine, Faculty of Medicine, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
- Medicine Department, King Fahad hospital of the University, Al-Khobar 31952, Saudi Arabia
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Guo SB, Du S, Cai KY, Cai HJ, Huang WJ, Tian XP. A scientometrics and visualization analysis of oxidative stress modulator Nrf2 in cancer profiles its characteristics and reveals its association with immune response. Heliyon 2023; 9:e17075. [PMID: 37342570 PMCID: PMC10277599 DOI: 10.1016/j.heliyon.2023.e17075] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2023] [Revised: 06/02/2023] [Accepted: 06/06/2023] [Indexed: 06/23/2023] Open
Abstract
Background Nrf2, an essential and fascinating transcription factor, enjoys a dual property in the occurrence and development of inflammation and cancer. For over two decades, numerous studies regarding Nrf2 in cancer have been reported, whereas there is still a lack of a scientometrics and visualization analysis of Nrf2 in cancer. Hence, a scientometric study regarding the oxidative stress modulator Nrf2 was implemented. Methods After the quality screening, we defined 7168 relevant studies from 2000 to 2021. CiteSpace, VOSviewer, R software, and GraphPad Prism were used for the following scientometric study and visualization analysis, including field profiles, research hotspots, and future predictions. Results The total number of publications and citations are 1058 and 54,690, respectively. After polynomial fitting curve analysis, two prediction functions of the annual publication number (y = 3.3909x2 - 13585x + 1 E+07) and citation number (185.45x2 - 743669x + 7 E+08) were generated. After scientometric analysis, we found that Biochemistry Molecular Biology correlates with Nrf2 in cancer highly, and Free Radical Biology and Medicine is a good choice for submitting Nrf2-related manuscripts. The current research hotspots of Nrf2 in cancer mainly focus on cancer therapy and its cellular and molecular mechanisms. "antioxidant response element (87.5)", "gene expression (43.98)", "antioxidant responsive element (21.14)", "chemoprevention (20.05)", "carcinogenesis (19.2)", "cancer chemoprevention (18.45)", "free radical (17.15)", "response element (14.17)", and "chemopreventive agent (14.04)" are important for cancer therapy study. In addition, "glutathione-S-transferase (47)", "keap1 (15.39)", and "heme oxygenase 1 gene (24.35)" are important for inflammation and cell fate study. More interestingly, by performing an "InfoMap" algorithm, the thematic map showed that the "immune response" is essential to oxidative stress modulator Nrf2 but not well developed, indicating it deserves further exploration. Conclusion This study revealed field profiles, research hotspots, and future directions of oxidative stress modulator Nrf2 in inflammation and cancer research, and our findings will offer a vigorous roadmap for further studies in this field.
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Affiliation(s)
- Song-Bin Guo
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
| | - Sheng Du
- State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, Guangzhou Institute of Respiratory Health, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, PR China
| | - Ke-Yu Cai
- Department of Colorectal and Anal Surgery, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, PR China
- Guangdong Provincial Key Laboratory of Colorectal and Pelvic Floor Diseases, Guangdong Institute of Gastroenterology, The Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510655, PR China
| | - Han-Jia Cai
- The Second Clinical Medical College, Guangzhou Medical University, Guangzhou, 511436, PR China
| | - Wei-Juan Huang
- Department of Pharmacology, College of Pharmacy, Jinan University, Guangzhou, 510632, PR China
| | - Xiao-Peng Tian
- Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
- State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Sun Yat-sen University Cancer Center, Guangzhou, 510060, PR China
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Alam MR, Seo KJ, Abdul-Ghafar J, Yim K, Lee SH, Jang HJ, Jung CK, Chong Y. Recent application of artificial intelligence on histopathologic image-based prediction of gene mutation in solid cancers. Brief Bioinform 2023; 24:bbad151. [PMID: 37114657 DOI: 10.1093/bib/bbad151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/24/2023] [Accepted: 03/24/2023] [Indexed: 04/29/2023] Open
Abstract
PURPOSE Evaluation of genetic mutations in cancers is important because distinct mutational profiles help determine individualized drug therapy. However, molecular analyses are not routinely performed in all cancers because they are expensive, time-consuming and not universally available. Artificial intelligence (AI) has shown the potential to determine a wide range of genetic mutations on histologic image analysis. Here, we assessed the status of mutation prediction AI models on histologic images by a systematic review. METHODS A literature search using the MEDLINE, Embase and Cochrane databases was conducted in August 2021. The articles were shortlisted by titles and abstracts. After a full-text review, publication trends, study characteristic analysis and comparison of performance metrics were performed. RESULTS Twenty-four studies were found mostly from developed countries, and their number is increasing. The major targets were gastrointestinal, genitourinary, gynecological, lung and head and neck cancers. Most studies used the Cancer Genome Atlas, with a few using an in-house dataset. The area under the curve of some of the cancer driver gene mutations in particular organs was satisfactory, such as 0.92 of BRAF in thyroid cancers and 0.79 of EGFR in lung cancers, whereas the average of all gene mutations was 0.64, which is still suboptimal. CONCLUSION AI has the potential to predict gene mutations on histologic images with appropriate caution. Further validation with larger datasets is still required before AI models can be used in clinical practice to predict gene mutations.
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Affiliation(s)
- Mohammad Rizwan Alam
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Kyung Jin Seo
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Jamshid Abdul-Ghafar
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Kwangil Yim
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Sung Hak Lee
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Hyun-Jong Jang
- Catholic Big Data Integration Center, Department of Physiology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Chan Kwon Jung
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
| | - Yosep Chong
- Department of Hospital Pathology, College of Medicine, The Catholic University of Korea, Seoul 06591, Republic of Korea
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Herrmann H. What's next for responsible artificial intelligence: a way forward through responsible innovation. Heliyon 2023; 9:e14379. [PMID: 36967876 PMCID: PMC10036946 DOI: 10.1016/j.heliyon.2023.e14379] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 03/03/2023] [Accepted: 03/03/2023] [Indexed: 03/29/2023] Open
Abstract
Industry is adopting artificial intelligence (AI) at a rapid pace and a growing number of countries have declared national AI strategies. However, several spectacular AI failures have led to ethical concerns about responsibility in AI development and use, which gave rise to the emerging field of responsible AI (RAI). The field of responsible innovation (RI) has a longer history and evolved toward a framework for the entire research, development, and innovation life cycle. However, this research demonstrates that the uptake of RI by RAI has been slow. RAI has been developing independently, with three times the number of publications than RI. The objective and knowledge contribution of this research was to understand how RAI has been developing independently from RI and contribute to how RI could be leveraged toward the progression of RAI in a causal loop diagram. It is concluded that stakeholder engagement of citizens from diverse cultures across the Global North and South is a policy leverage point for moving the RI adoption by RAI toward global best practice. A role-specific recommendation for policy makers is made to deploy modes of engaging with the Global South with more urgency to avoid the risk of harming vulnerable populations. As an additional methodological contribution, this study employs a novel method, systematic science mapping, which combines systematic literature reviews with science mapping. This new method enabled the discovery of an emerging 'axis of adoption' of RI by RAI around the thematic areas of ethics, governance, stakeholder engagement, and sustainability. 828 Scopus articles were mapped for RI and 2489 articles were mapped for RAI. The research presented here is by any measure the largest systematic literature review of both fields to date and the only cross-disciplinary review from a methodological perspective.
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Affiliation(s)
- Heinz Herrmann
- Torrens University Australia, 17/51 Foveaux St, Surry Hills NSW, 2010, Australia
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Su M, Guo J, Chen H, Huang J. Developing a machine learning prediction algorithm for early differentiation of urosepsis from urinary tract infection. Clin Chem Lab Med 2023; 61:521-529. [PMID: 36383696 DOI: 10.1515/cclm-2022-1006] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Accepted: 11/06/2022] [Indexed: 11/18/2022]
Abstract
OBJECTIVES Early recognition and timely intervention for urosepsis are key to reducing morbidity and mortality. Blood culture has low sensitivity, and a long turnaround time makes meeting the needs of clinical diagnosis difficult. This study aimed to use biomarkers to build a machine learning model for early prediction of urosepsis. METHODS Through retrospective analysis, we screened 157 patients with urosepsis and 417 patients with urinary tract infection. Laboratory data of the study participants were collected, including data on biomarkers, such as procalcitonin, D-dimer, and C-reactive protein. We split the data into training (80%) and validation datasets (20%) and determined the average model prediction accuracy through cross-validation. RESULTS In total, 26 variables were initially screened and 18 were statistically significant. The influence of the 18 variables was sorted using three ranking methods to further determine the best combination of variables. The Gini importance ranking method was found to be suitable for variable filtering. The accuracy rates of the six machine learning models in predicting urosepsis were all higher than 80%, and the performance of the artificial neural network (ANN) was the best among all. When the ANN included the eight biomarkers with the highest influence ranking, its model had the best prediction performance, with an accuracy rate of 92.9% and an area under the receiver operating characteristic curve of 0.946. CONCLUSIONS Urosepsis can be predicted using only the top eight biomarkers determined by the ranking method. This data-driven predictive model will enable clinicians to make quick and accurate diagnoses.
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Affiliation(s)
- Mingkuan Su
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, P.R. China
| | - Jianfeng Guo
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, P.R. China
| | - Hongbin Chen
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, P.R. China
| | - Jiancheng Huang
- Department of Laboratory Medicine, Mindong Hospital Affiliated to Fujian Medical University, Fuan City, P.R. China
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Oosterhoff JHF, Karhade AV, Groot OQ, Schwab JH, Heng M, Klang E, Prat D. Intercontinental validation of a clinical prediction model for predicting 90-day and 2-year mortality in an Israeli cohort of 2033 patients with a femoral neck fracture aged 65 or above. Eur J Trauma Emerg Surg 2023; 49:1545-1553. [PMID: 36757419 DOI: 10.1007/s00068-023-02237-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2022] [Accepted: 01/27/2023] [Indexed: 02/10/2023]
Abstract
PURPOSE Mortality prediction in elderly femoral neck fracture patients is valuable in treatment decision-making. A previously developed and internally validated clinical prediction model shows promise in identifying patients at risk of 90-day and 2-year mortality. Validation in an independent cohort is required to assess the generalizability; especially in geographically distinct regions. Therefore we questioned, is the SORG Orthopaedic Research Group (SORG) femoral neck fracture mortality algorithm externally valid in an Israeli cohort to predict 90-day and 2-year mortality? METHODS We previously developed a prediction model in 2022 for estimating the risk of mortality in femoral neck fracture patients using a multicenter institutional cohort of 2,478 patients from the USA. The model included the following input variables that are available on clinical admission: age, male gender, creatinine level, absolute neutrophil, hemoglobin level, international normalized ratio (INR), congestive heart failure (CHF), displaced fracture, hemiplegia, chronic obstructive pulmonary disease (COPD), history of cerebrovascular accident (CVA) and beta-blocker use. To assess the generalizability, we used an intercontinental institutional cohort from the Sheba Medical Center in Israel (level I trauma center), queried between June 2008 and February 2022. Generalizability of the model was assessed using discrimination, calibration, Brier score, and decision curve analysis. RESULTS The validation cohort included 2,033 patients, aged 65 years or above, that underwent femoral neck fracture surgery. Most patients were female 64.8% (n = 1317), the median age was 81 years (interquartile range = 75-86), and 80.4% (n = 1635) patients sustained a displaced fracture (Garden III/IV). The 90-day mortality was 9.4% (n = 190) and 2-year mortality was 30.0% (n = 610). Despite numerous baseline differences, the model performed acceptably to the validation cohort on discrimination (c-statistic 0.67 for 90-day, 0.67 for 2-year), calibration, Brier score, and decision curve analysis. CONCLUSIONS The previously developed SORG femoral neck fracture mortality algorithm demonstrated good performance in an independent intercontinental population. Current iteration should not be relied on for patient care, though suggesting potential utility in assessing patients at low risk for 90-day or 2-year mortality. Further studies should evaluate this tool in a prospective setting and evaluate its feasibility and efficacy in clinical practice. The algorithm can be freely accessed: https://sorg-apps.shinyapps.io/hipfracturemortality/ . LEVEL OF EVIDENCE Level III, Prognostic study.
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Affiliation(s)
- Jacobien H F Oosterhoff
- Department of Orthopaedic Surgery, Amsterdam Movement Sciences, Amsterdam University Medical Centers, University of Amsterdam, Meibergdreef 9, 1105AZ, Amsterdam, the Netherlands. .,Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA. .,Department Engineering Systems and Services, Faculty Technology Policy and Management, Delft University of Technology, Delft, The Netherlands.
| | - Aditya V Karhade
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Olivier Q Groot
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Joseph H Schwab
- Department of Orthopaedic Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Marilyn Heng
- Department of Orthopaedic Surgery, University of Miami Miller School of Medicine, Miami, FL, USA.,Orthopaedic Trauma Service, Jackson Memorial Ryder Trauma Center, Miami, FL, USA
| | - Eyal Klang
- Sami Sagol AI Hub, ARC, Sheba Medical Center, Ramat Gan, Israel
| | - Dan Prat
- Department of Orthopaedic Surgery, Sheba Medical Center, Ramat Gan, Israel
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Ha JY, Park HJ. [Keyword Network Analysis and Topic Modeling of News Articles Related to Artificial Intelligence and Nursing]. J Korean Acad Nurs 2023; 53:55-68. [PMID: 36898685 DOI: 10.4040/jkan.22117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2022] [Revised: 01/09/2023] [Accepted: 02/08/2023] [Indexed: 03/09/2023]
Abstract
PURPOSE The purpose of this study was to identify the main keywords, network properties, and main topics of news articles related to artificial intelligence technology in the field of nursing. METHODS After collecting artificial intelligence-and nursing-related news articles published between January 1, 1991, and July 24, 2022, keywords were extracted via preprocessing. A total of 3,267 articles were searched, and 2,996 were used for the final analysis. Text network analysis and topic modeling were performed using NetMiner 4.4. RESULTS As a result of analyzing the frequency of appearance, the keywords used most frequently were education, medical robot, telecom, dementia, and the older adults living alone. Keyword network analysis revealed the following results: a density of 0.002, an average degree of 8.79, and an average distance of 2.43; the central keywords identified were 'education,' 'medical robot,' and 'fourth industry.' Five topics were derived from news articles related to artificial intelligence and nursing: 'Artificial intelligence nursing research and development in the health and medical field,' 'Education using artificial intelligence for children and youth care,' 'Nursing robot for older adults care,' 'Community care policy and artificial intelligence,' and 'Smart care technology in an aging society.' CONCLUSION The use of artificial intelligence may be helpful among the local community, older adult, children, and adolescents. In particular, health management using artificial intelligence is indispensable now that we are facing a super-aging society. In the future, studies on nursing intervention and development of nursing programs using artificial intelligence should be conducted.
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Affiliation(s)
- Ju-Young Ha
- College of Nursing, Pusan National University, Yangsan, Korea
| | - Hyo-Jin Park
- College of Nursing, Pusan National University, Yangsan, Korea.
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47
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Karger E, Kureljusic M. Artificial Intelligence for Cancer Detection-A Bibliometric Analysis and Avenues for Future Research. Curr Oncol 2023; 30:1626-1647. [PMID: 36826086 PMCID: PMC9954989 DOI: 10.3390/curroncol30020125] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Revised: 01/18/2023] [Accepted: 01/27/2023] [Indexed: 01/31/2023] Open
Abstract
After cardiovascular diseases, cancer is responsible for the most deaths worldwide. Detecting a cancer disease early improves the chances for healing significantly. One group of technologies that is increasingly applied for detecting cancer is artificial intelligence. Artificial intelligence has great potential to support clinicians and medical practitioners as it allows for the early detection of carcinomas. During recent years, research on artificial intelligence for cancer detection grew a lot. Within this article, we conducted a bibliometric study of the existing research dealing with the application of artificial intelligence in cancer detection. We analyzed 6450 articles on that topic that were published between 1986 and 2022. By doing so, we were able to give an overview of this research field, including its key topics, relevant outlets, institutions, and articles. Based on our findings, we developed a future research agenda that can help to advance research on artificial intelligence for cancer detection. In summary, our study is intended to serve as a platform and foundation for researchers that are interested in the potential of artificial intelligence for detecting cancer.
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Affiliation(s)
- Erik Karger
- Information Systems and Strategic IT Management, University of Duisburg-Essen, 45141 Essen, Germany
| | - Marko Kureljusic
- International Accounting, University of Duisburg-Essen, 45141 Essen, Germany
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Moshawrab M, Adda M, Bouzouane A, Ibrahim H, Raad A. Smart Wearables for the Detection of Cardiovascular Diseases: A Systematic Literature Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23020828. [PMID: 36679626 PMCID: PMC9865666 DOI: 10.3390/s23020828] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 12/27/2022] [Accepted: 01/09/2023] [Indexed: 06/02/2023]
Abstract
Background: The advancement of information and communication technologies and the growing power of artificial intelligence are successfully transforming a number of concepts that are important to our daily lives. Many sectors, including education, healthcare, industry, and others, are benefiting greatly from the use of such resources. The healthcare sector, for example, was an early adopter of smart wearables, which primarily serve as diagnostic tools. In this context, smart wearables have demonstrated their effectiveness in detecting and predicting cardiovascular diseases (CVDs), the leading cause of death worldwide. Objective: In this study, a systematic literature review of smart wearable applications for cardiovascular disease detection and prediction is presented. After conducting the required search, the documents that met the criteria were analyzed to extract key criteria such as the publication year, vital signs recorded, diseases studied, hardware used, smart models used, datasets used, and performance metrics. Methods: This study followed the PRISMA guidelines by searching IEEE, PubMed, and Scopus for publications published between 2010 and 2022. Once records were located, they were reviewed to determine which ones should be included in the analysis. Finally, the analysis was completed, and the relevant data were included in the review along with the relevant articles. Results: As a result of the comprehensive search procedures, 87 papers were deemed relevant for further review. In addition, the results are discussed to evaluate the development and use of smart wearable devices for cardiovascular disease management, and the results demonstrate the high efficiency of such wearable devices. Conclusions: The results clearly show that interest in this topic has increased. Although the results show that smart wearables are quite accurate in detecting, predicting, and even treating cardiovascular disease, further research is needed to improve their use.
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Affiliation(s)
- Mohammad Moshawrab
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Mehdi Adda
- Département de Mathématiques, Informatique et Génie, Université du Québec à Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
| | - Abdenour Bouzouane
- Département d’Informatique et de Mathématique, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Chicoutimi, QC G7H 2B1, Canada
| | - Hussein Ibrahim
- Institut Technologique de Maintenance Industrielle, 175 Rue de la Vérendrye, Sept-Îles, QC G4R 5B7, Canada
| | - Ali Raad
- Faculty of Arts & Sciences, Islamic University of Lebanon, Wardaniyeh P.O. Box 30014, Lebanon
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Ahmad MN, Abdallah SA, Abbasi SA, Abdallah AM. Student perspectives on the integration of artificial intelligence into healthcare services. Digit Health 2023; 9:20552076231174095. [PMID: 37312954 PMCID: PMC10259127 DOI: 10.1177/20552076231174095] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2022] [Accepted: 04/19/2023] [Indexed: 06/15/2023] Open
Abstract
Background Healthcare workers are often overworked, underfunded, and face many challenges. Integration of artificial intelligence into healthcare service provision can tackle these challenges by relieving burdens on healthcare workers. Since healthcare students are our future healthcare workers, we assessed the knowledge, attitudes, and perspectives of current healthcare students at Qatar University on the implementation of artificial intelligence into healthcare services. Methods This was a cross-sectional study of QU-Health Cluster students via an online survey over a three-week period in November 2021. Chi-squared tests and gamma coefficients were used to compare differences between categorical variables. Results One hundred and ninety-three QU-Health students responded. Most participants had a positive attitude towards artificial intelligence, finding it useful and reliable. The most popular perceived advantage of artificial intelligence was its ability to speed up work processes. Around 40% expressed concern about a threat to job security from artificial intelligence, and a majority believed that artificial intelligence cannot provide sympathetic care (57.9%). Participants who felt that artificial intelligence can better make diagnoses than humans also agreed that artificial intelligence could replace their job (p = 0.005). Male students had more knowledge (p = 0.005) and received more training (p = 0.005) about healthcare artificial intelligence. Participants cited a lack of expert mentorship as a barrier to obtaining knowledge about artificial intelligence, followed by lack of dedicated courses and funding. Conclusions More resources are required for students to develop a good understanding about artificial intelligence. Education needs to be supported by expert mentorship. Further work is needed on how best to integrate artificial intelligence teaching into university curricula.
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Affiliation(s)
- Muna N Ahmad
- Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Saja A Abdallah
- University of Birmingham Medical School, Edgbaston Campus, Birmingham, UK
| | - Saddam A Abbasi
- Department of Mathematics, Statistics, and Physics, Qatar University, Doha, Qatar
- Statistical Consulting Unit, College of Arts and Science, Qatar University, Doha, Qatar
| | - Atiyeh M Abdallah
- Department of Biomedical Sciences, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
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50
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Al-Ani MA, Bai C, Hashky A, Parker AM, Vilaro JR, Aranda JM, Shickel B, Rashidi P, Bihorac A, Ahmed MM, Mardini MT. Artificial intelligence guidance of advanced heart failure therapies: A systematic scoping review. Front Cardiovasc Med 2023; 10:1127716. [PMID: 36910520 PMCID: PMC9999024 DOI: 10.3389/fcvm.2023.1127716] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2022] [Accepted: 02/07/2023] [Indexed: 03/14/2023] Open
Abstract
Introduction Artificial intelligence can recognize complex patterns in large datasets. It is a promising technology to advance heart failure practice, as many decisions rely on expert opinions in the absence of high-quality data-driven evidence. Methods We searched Embase, Web of Science, and PubMed databases for articles containing "artificial intelligence," "machine learning," or "deep learning" and any of the phrases "heart transplantation," "ventricular assist device," or "cardiogenic shock" from inception until August 2022. We only included original research addressing post heart transplantation (HTx) or mechanical circulatory support (MCS) clinical care. Review and data extraction were performed in accordance with PRISMA-Scr guidelines. Results Of 584 unique publications detected, 31 met the inclusion criteria. The majority focused on outcome prediction post HTx (n = 13) and post durable MCS (n = 7), as well as post HTx and MCS management (n = 7, n = 3, respectively). One study addressed temporary mechanical circulatory support. Most studies advocated for rapid integration of AI into clinical practice, acknowledging potential improvements in management guidance and reliability of outcomes prediction. There was a notable paucity of external data validation and integration of multiple data modalities. Conclusion Our review showed mounting innovation in AI application in management of MCS and HTx, with the largest evidence showing improved mortality outcome prediction.
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Affiliation(s)
- Mohammad A Al-Ani
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Chen Bai
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
| | - Amal Hashky
- Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL, United States
| | - Alex M Parker
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Juan R Vilaro
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Juan M Aranda
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Benjamin Shickel
- Department of Medicine, University of Florida, Gainesville, FL, United States.,Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States
| | - Parisa Rashidi
- Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States.,Department of Biomedical Engineering, University of Florida, Gainesville, FL, United States
| | - Azra Bihorac
- Department of Medicine, University of Florida, Gainesville, FL, United States.,Intelligent Critical Care Center (IC3), University of Florida, Gainesville, FL, United States
| | - Mustafa M Ahmed
- Division of Cardiovascular Medicine, University of Florida, Gainesville, FL, United States
| | - Mamoun T Mardini
- Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, FL, United States
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