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Alnasser AH, Hassanain MA, Alnasser MA, Alnasser AH. Critical factors challenging the integration of AI technologies in healthcare workplaces: a stakeholder assessment. J Health Organ Manag 2024; ahead-of-print. [PMID: 39300711 DOI: 10.1108/jhom-04-2024-0135] [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: 09/22/2024]
Abstract
PURPOSE This study aims to identify and assess the factors challenging the integration of artificial intelligence (AI) technologies in healthcare workplaces. DESIGN/METHODOLOGY/APPROACH The study utilized a mixed approach, that starts with a literature review, then developing and testing a questionnaire survey of the factors challenging the integration of AI technologies in healthcare workplaces. In total, 46 factors were identified and classified under 6 groups. These factors were assessed by four different stakeholder categories: facilities managers, medical staff, operational staff and patients/visitors. The evaluations gathered were examined to determine the relative importance index (RII), importance rating (IR) and ranking of each factor. FINDINGS All 46 factors were assessed as "Very Important" through the overall assessment by the four stakeholder categories. The results indicated that the most important factors, across all groups, are "AI ability to learn from patient data", "insufficient data privacy measures for patients", "availability of technical support and maintenance services", "physicians' acceptance of AI in healthcare", "reliability and uptime of AI systems" and "ability to reduce medical errors". PRACTICAL IMPLICATIONS Determining the importance ratings of the factors can lead to better resource allocation and the development of strategies to facilitate the adoption and implementation of these technologies, thus promoting the development of innovative solutions to improve healthcare practices. ORIGINALITY/VALUE This study contributes to the body of knowledge in the domain of technology adoption and implementation in the medical workplace, through improving stakeholders' comprehension of the factors challenging the integration of AI technologies.
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Affiliation(s)
- Abdullah H Alnasser
- Architectural Engineering and Construction Management Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | - Mohammad A Hassanain
- Architectural Engineering and Construction Management Department, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
- Interdisciplinary Research Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia
| | | | - Ali H Alnasser
- Primary Healthcare Units, Al Ahsa Health Cluster, Al Ahsa, Saudi Arabia
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Finkelstein J, Gabriel A, Schmer S, Truong TT, Dunn A. Identifying Facilitators and Barriers to Implementation of AI-Assisted Clinical Decision Support in an Electronic Health Record System. J Med Syst 2024; 48:89. [PMID: 39292314 PMCID: PMC11410896 DOI: 10.1007/s10916-024-02104-9] [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: 05/23/2024] [Accepted: 08/29/2024] [Indexed: 09/19/2024]
Abstract
Recent advancements in computing have led to the development of artificial intelligence (AI) enabled healthcare technologies. AI-assisted clinical decision support (CDS) integrated into electronic health records (EHR) was demonstrated to have a significant potential to improve clinical care. With the rapid proliferation of AI-assisted CDS, came the realization that a lack of careful consideration of socio-technical issues surrounding the implementation and maintenance of these tools can result in unanticipated consequences, missed opportunities, and suboptimal uptake of these potentially useful technologies. The 48-h Discharge Prediction Tool (48DPT) is a new AI-assisted EHR CDS to facilitate discharge planning. This study aimed to methodologically assess the implementation of 48DPT and identify the barriers and facilitators of adoption and maintenance using the validated implementation science frameworks. The major dimensions of RE-AIM (Reach, Effectiveness, Adoption, Implementation, Maintenance) and the constructs of the Consolidated Framework for Implementation Research (CFIR) frameworks have been used to analyze interviews of 24 key stakeholders using 48DPT. The systematic assessment of the 48DPT implementation allowed us to describe facilitators and barriers to implementation such as lack of awareness, lack of accuracy and trust, limited accessibility, and transparency. Based on our evaluation, the factors that are crucial for the successful implementation of AI-assisted EHR CDS were identified. Future implementation efforts of AI-assisted EHR CDS should engage the key clinical stakeholders in the AI tool development from the very inception of the project, support transparency and explainability of the AI models, provide ongoing education and onboarding of the clinical users, and obtain continuous input from clinical staff on the CDS performance.
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Affiliation(s)
- Joseph Finkelstein
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Rm. 2028, Salt Lake City, UT, 84108, USA.
| | - Aileen Gabriel
- Department of Biomedical Informatics, University of Utah, 421 Wakara Way, Rm. 2028, Salt Lake City, UT, 84108, USA
| | - Susanna Schmer
- Department of Case Management, Mount Sinai Health System, New York, NY, USA
| | - Tuyet-Trinh Truong
- Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew Dunn
- Division of Hospital Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
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3
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Al-Khalifa KS, Ahmed WM, Azhari AA, Qaw M, Alsheikh R, Alqudaihi F, Alfaraj A. The Use of Artificial Intelligence in Caries Detection: A Review. Bioengineering (Basel) 2024; 11:936. [PMID: 39329679 PMCID: PMC11428802 DOI: 10.3390/bioengineering11090936] [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: 07/07/2024] [Revised: 08/20/2024] [Accepted: 09/11/2024] [Indexed: 09/28/2024] Open
Abstract
Advancements in artificial intelligence (AI) have significantly impacted the field of dentistry, particularly in diagnostic imaging for caries detection. This review critically examines the current state of AI applications in caries detection, focusing on the performance and accuracy of various AI techniques. We evaluated 40 studies from the past 23 years, carefully selected for their relevance and quality. Our analysis highlights the potential of AI, especially convolutional neural networks (CNNs), to improve diagnostic accuracy and efficiency in detecting dental caries. The findings underscore the transformative potential of AI in clinical dental practice.
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Affiliation(s)
- Khalifa S. Al-Khalifa
- Department of Preventive Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia
| | - Walaa Magdy Ahmed
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (W.M.A.); (A.A.A.)
| | - Amr Ahmed Azhari
- Department of Restorative Dentistry, Faculty of Dentistry, King Abdulaziz University, Jeddah 21589, Saudi Arabia; (W.M.A.); (A.A.A.)
| | - Masoumah Qaw
- Department of Restorative Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (M.Q.); (R.A.)
| | - Rasha Alsheikh
- Department of Restorative Dental Sciences, College of Dentistry, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia; (M.Q.); (R.A.)
| | - Fatema Alqudaihi
- Department of Restorative Dentistry, Khobar Dental Complex, Eastern Health Cluster, Dammam 32253, Saudi Arabia;
| | - Amal Alfaraj
- Department of Prosthodontics and Dental Implantology, College of Dentistry, King Faisal University, Al-Ahsa 31982, Saudi Arabia;
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Zhang D, Sheng Y, Wang C, Chen W, Shi X. Global traumatic brain injury intracranial pressure: from monitoring to surgical decision. Front Neurol 2024; 15:1423329. [PMID: 39355091 PMCID: PMC11442239 DOI: 10.3389/fneur.2024.1423329] [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: 04/25/2024] [Accepted: 09/02/2024] [Indexed: 10/03/2024] Open
Abstract
Traumatic brain injury (TBI) is a significant global public health issue, heavily impacting human health, especially in low-and middle-income areas. Despite numerous guidelines and consensus statements, TBI fatality rates remain high. The pathogenesis of severe TBI is closely linked to rising intracranial pressure (ICP). Elevated intracranial pressure can lead to cerebral herniation, resulting in respiratory and circulatory collapse, and ultimately, death. Managing intracranial pressure (ICP) is crucial in neuro-intensive care. Timely diagnosis and precise treatment of elevated ICP are essential. ICP monitoring provides real-time insights into a patient's condition, offering invaluable guidance for comprehensive management. ICP monitoring and standardization can effectively reduce secondary nerve damage, lowering morbidity and mortality rates. Accurately assessing and using true ICP values to manage TBI patients still depends on doctors' clinical experience. This review discusses: (a) Epidemiological disparities of traumatic brain injuries across countries with different income levels worldwide; (b) The significance and function of ICP monitoring; (c) Current status and challenges of ICP monitoring; (d) The impact of decompressive craniectomy on reducing intracranial pressure; and (e) Management of TBI in diverse income countries. We suggest a thorough evaluation of ICP monitoring, head CT findings, and GCS scores before deciding on decompressive craniectomy. Personalized treatment should be emphasized to assess the need for surgical decompression in TBI patients, offering crucial insights for clinical decision-making.
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Affiliation(s)
- Dan Zhang
- Longgang Central Hospital of Shenzhen, Guangdong, China
| | - Yanzhi Sheng
- Shenzhen College of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangdong, China
| | - Chengbin Wang
- Shenzhen College of Clinical Medicine, Guangzhou University of Chinese Medicine, Guangdong, China
| | - Wei Chen
- Longgang Central Hospital of Shenzhen, Guangdong, China
| | - Xiaofeng Shi
- Longgang Central Hospital of Shenzhen, Guangdong, China
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Seth I, Lim B, Phan R, Xie Y, Kenney PS, Bukret WE, Thomsen JB, Cuomo R, Ross RJ, Ng SKH, Rozen WM. Perforator Selection with Computed Tomography Angiography for Unilateral Breast Reconstruction: A Clinical Multicentre Analysis. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:1500. [PMID: 39336540 PMCID: PMC11433981 DOI: 10.3390/medicina60091500] [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: 08/04/2024] [Revised: 08/30/2024] [Accepted: 09/11/2024] [Indexed: 09/30/2024]
Abstract
Background and Objectives: Despite CTAs being critical for preoperative planning in autologous breast reconstruction, experienced plastic surgeons may have differing preferences for which side of the abdomen to use for unilateral breast reconstruction. Large language models (LLMs) have the potential to assist medical imaging interpretation. This study compares the perforator selection preferences of experienced plastic surgeons with four popular LLMs based on CTA images for breast reconstruction. Materials and Methods: Six experienced plastic surgeons from Australia, the US, Italy, Denmark, and Argentina reviewed ten CTA images, indicated their preferred side of the abdomen for unilateral breast reconstruction and recommended the type of autologous reconstruction. The LLMs were prompted to do the same. The average decisions were calculated, recorded in suitable tables, and compared. Results: The six consultants predominantly recommend the DIEP procedure (83%). This suggests experienced surgeons feel more comfortable raising DIEP than TRAM flaps, which they recommended only 3% of the time. They also favoured MS TRAM and SIEA less frequently (11% and 2%, respectively). Three LLMs-ChatGPT-4o, ChatGPT-4, and Bing CoPilot-exclusively recommended DIEP (100%), while Claude suggested DIEP 90% and MS TRAM 10%. Despite minor variations in side recommendations, consultants and AI models clearly preferred DIEP. Conclusions: Consultants and LLMs consistently preferred DIEP procedures, indicating strong confidence among experienced surgeons, though LLMs occasionally deviated in recommendations, highlighting limitations in their image interpretation capabilities. This emphasises the need for ongoing refinement of AI-assisted decision support systems to ensure they align more closely with expert clinical judgment and enhance their reliability in clinical practice.
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Affiliation(s)
- Ishith Seth
- Department of Plastic and Reconstructive Surgery, Peninsula Health, Melbourne 3199, Australia
| | - Bryan Lim
- Department of Plastic and Reconstructive Surgery, Peninsula Health, Melbourne 3199, Australia
| | - Robert Phan
- Department of Plastic and Reconstructive Surgery, Peninsula Health, Melbourne 3199, Australia
| | - Yi Xie
- Department of Plastic and Reconstructive Surgery, Peninsula Health, Melbourne 3199, Australia
| | - Peter Sinkjær Kenney
- Department of Plastic and Reconstructive Surgery, Odense University Hospital, 5000 Odense, Denmark
| | - William E. Bukret
- Department of Plastic and Reconstructive Surgery, UNC School of Medicine, Chapel Hill, NC 27599, USA
| | - Jørn Bo Thomsen
- Department of Plastic and Reconstructive Surgery, Odense University Hospital, 5000 Odense, Denmark
| | - Roberto Cuomo
- Plastic Surgery Unit, Department of Medicine, Surgery and Neuroscience, University of Siena, 53100 Siena, Italy
| | - Richard J. Ross
- Department of Plastic and Reconstructive Surgery, Peninsula Health, Melbourne 3199, Australia
| | - Sally Kiu-Huen Ng
- Department of Plastic and Reconstructive Surgery, The Austin Health, Melbourne 3084, Australia
| | - Warren M. Rozen
- Department of Plastic and Reconstructive Surgery, Peninsula Health, Melbourne 3199, Australia
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Tyskbo D, Nygren J. Reconfiguration of uncertainty: Introducing AI for prediction of mortality at the emergency department. Soc Sci Med 2024; 359:117298. [PMID: 39260029 DOI: 10.1016/j.socscimed.2024.117298] [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: 08/21/2023] [Revised: 09/02/2024] [Accepted: 09/04/2024] [Indexed: 09/13/2024]
Abstract
The promise behind many advanced digital technologies in healthcare is to provide novel and accurate information, aiding medical experts to navigate and, ultimately, decrease uncertainty in their clinical work. However, sociological studies have started to show that these technologies are not producing straightforward objective knowledge, but instead often become associated with new uncertainties arising in unanticipated places and situations. This study contributes to the body of work by presenting a qualitative study of an Artificial Intelligence (AI) algorithm designed to predict the risk of mortality in patients discharged to home from the emergency department (ED). Through in-depth interviews with physicians working at the ED of a Swedish hospital, we demonstrate that while the AI algorithm can reduce targeted uncertainty, it simultaneously introduces three new forms of uncertainty into clinical practice: epistemic uncertainty, actionable uncertainty and ethical uncertainty. These new uncertainties require deliberate management and control, marking a shift from the physicians' accustomed comfort with uncertainty in mortality prediction. Our study advances the understanding of the recursive nature and temporal dynamics of uncertainty in medical work, showing how new uncertainties emerge from attempts to manage existing ones. It also reveals that physicians' attitudes towards, and management of, uncertainty vary depending on its form and underscores the intertwined role of digital technology in this process. By examining AI in emergency care, we provide valuable insights into how this epistemic technology reconfigures clinical uncertainty, offering significant theoretical and practical implications for the integration of AI in healthcare.
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Affiliation(s)
- Daniel Tyskbo
- School of Health and Welfare, Halmstad University, Box 823, SE-301 18, Halmstad, Sweden.
| | - Jens Nygren
- School of Health and Welfare, Halmstad University, Box 823, SE-301 18, Halmstad, Sweden.
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Janssen SM, Bouzembrak Y, Tekinerdogan B. Artificial Intelligence in Malnutrition: A Systematic Literature Review. Adv Nutr 2024; 15:100264. [PMID: 38971229 PMCID: PMC11403436 DOI: 10.1016/j.advnut.2024.100264] [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: 04/12/2024] [Revised: 06/03/2024] [Accepted: 06/26/2024] [Indexed: 07/08/2024] Open
Abstract
Malnutrition among the population of the world is a frequent yet underdiagnosed problem in both children and adults. Development of malnutrition screening and diagnostic tools for early detection of malnutrition is necessary to prevent long-term complications to patients' health and well-being. Most of these tools are based on predefined questionnaires and consensus guidelines. The use of artificial intelligence (AI) allows for automated tools to detect malnutrition in an earlier stage to prevent long-term consequences. In this study, a systematic literature review was carried out with the goal of providing detailed information on what patient groups, screening tools, machine learning algorithms, data types, and variables are being used, as well as the current limitations and implementation stage of these AI-based tools. The results showed that a staggering majority exceeding 90% of all AI models go unused in day-to-day clinical practice. Furthermore, supervised learning models seemed to be the most popular type of learning. Alongside this, disease-related malnutrition was the most common category of malnutrition found in the analysis of all primary studies. This research provides a resource for researchers to identify directions for their research on the use of AI in malnutrition.
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Affiliation(s)
- Sander Mw Janssen
- Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands
| | - Yamine Bouzembrak
- Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands.
| | - Bedir Tekinerdogan
- Information Technology Group, Wageningen University and Research, Wageningen, The Netherlands
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Ong W, Lee A, Tan WC, Fong KTD, Lai DD, Tan YL, Low XZ, Ge S, Makmur A, Ong SJ, Ting YH, Tan JH, Kumar N, Hallinan JTPD. Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging-A Systematic Review. Cancers (Basel) 2024; 16:2988. [PMID: 39272846 PMCID: PMC11394591 DOI: 10.3390/cancers16172988] [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: 07/10/2024] [Revised: 08/14/2024] [Accepted: 08/26/2024] [Indexed: 09/15/2024] Open
Abstract
In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications in CT imaging for spinal tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused on detecting spinal malignancies, 11 (33.3%) on classification, 6 (18.2%) on prognostication, 3 (9.1%) on treatment planning, and 1 (3.0%) on both detection and classification. Of the classification studies, 7 (21.2%) used machine learning to distinguish between benign and malignant lesions, 3 (9.1%) evaluated tumor stage or grade, and 2 (6.1%) employed radiomics for biomarker classification. Prognostic studies included three (9.1%) that predicted complications such as pathological fractures and three (9.1%) that predicted treatment outcomes. AI's potential for improving workflow efficiency, aiding decision-making, and reducing complications is discussed, along with its limitations in generalizability, interpretability, and clinical integration. Future directions for AI in spinal oncology are also explored. In conclusion, while AI technologies in CT imaging are promising, further research is necessary to validate their clinical effectiveness and optimize their integration into routine practice.
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Affiliation(s)
- Wilson Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Aric Lee
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Wei Chuan Tan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Kuan Ting Dominic Fong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Daoyong David Lai
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Yi Liang Tan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
| | - Xi Zhen Low
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Shuliang Ge
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Andrew Makmur
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Shao Jin Ong
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Yong Han Ting
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
| | - Jiong Hao Tan
- National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - Naresh Kumar
- National University Spine Institute, Department of Orthopaedic Surgery, National University Health System, 1E, Lower Kent Ridge Road, Singapore 119228, Singapore
| | - James Thomas Patrick Decourcy Hallinan
- Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074, Singapore
- Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, 10 Medical Drive, Singapore 117597, Singapore
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Nair M, Svedberg P, Larsson I, Nygren JM. A comprehensive overview of barriers and strategies for AI implementation in healthcare: Mixed-method design. PLoS One 2024; 19:e0305949. [PMID: 39121051 PMCID: PMC11315296 DOI: 10.1371/journal.pone.0305949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 06/07/2024] [Indexed: 08/11/2024] Open
Abstract
Implementation of artificial intelligence systems for healthcare is challenging. Understanding the barriers and implementation strategies can impact their adoption and allows for better anticipation and planning. This study's objective was to create a detailed inventory of barriers to and strategies for AI implementation in healthcare to support advancements in methods and implementation processes in healthcare. A sequential explanatory mixed method design was used. Firstly, scoping reviews and systematic literature reviews were identified using PubMed. Selected studies included empirical cases of AI implementation and use in clinical practice. As the reviews were deemed insufficient to fulfil the aim of the study, data collection shifted to the primary studies included in those reviews. The primary studies were screened by title and abstract, and thereafter read in full text. Then, data on barriers to and strategies for AI implementation were extracted from the included articles, thematically coded by inductive analysis, and summarized. Subsequently, a direct qualitative content analysis of 69 interviews with healthcare leaders and healthcare professionals confirmed and added results from the literature review. Thirty-eight empirical cases from the six identified scoping and literature reviews met the inclusion and exclusion criteria. Barriers to and strategies for AI implementation were grouped under three phases of implementation (planning, implementing, and sustaining the use) and were categorized into eleven concepts; Leadership, Buy-in, Change management, Engagement, Workflow, Finance and human resources, Legal, Training, Data, Evaluation and monitoring, Maintenance. Ethics emerged as a twelfth concept through qualitative analysis of the interviews. This study illustrates the inherent challenges and useful strategies in implementing AI in healthcare practice. Future research should explore various aspects of leadership, collaboration and contracts among key stakeholders, legal strategies surrounding clinicians' liability, solutions to ethical dilemmas, infrastructure for efficient integration of AI in workflows, and define decision points in the implementation process.
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Affiliation(s)
- Monika Nair
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Ingrid Larsson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens M. Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Siira E, Tyskbo D, Nygren J. Healthcare leaders' experiences of implementing artificial intelligence for medical history-taking and triage in Swedish primary care: an interview study. BMC PRIMARY CARE 2024; 25:268. [PMID: 39048973 PMCID: PMC11267767 DOI: 10.1186/s12875-024-02516-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2024] [Accepted: 07/08/2024] [Indexed: 07/27/2024]
Abstract
BACKGROUND Artificial intelligence (AI) holds significant promise for enhancing the efficiency and safety of medical history-taking and triage within primary care. However, there remains a dearth of knowledge concerning the practical implementation of AI systems for these purposes, particularly in the context of healthcare leadership. This study explores the experiences of healthcare leaders regarding the barriers to implementing an AI application for automating medical history-taking and triage in Swedish primary care, as well as the actions they took to overcome these barriers. Furthermore, the study seeks to provide insights that can inform the development of AI implementation strategies for healthcare. METHODS We adopted an inductive qualitative approach, conducting semi-structured interviews with 13 healthcare leaders representing seven primary care units across three regions in Sweden. The collected data were subsequently analysed utilizing thematic analysis. Our study adhered to the Consolidated Criteria for Reporting Qualitative Research to ensure transparent and comprehensive reporting. RESULTS The study identified implementation barriers encountered by healthcare leaders across three domains: (1) healthcare professionals, (2) organization, and (3) technology. The first domain involved professional scepticism and resistance, the second involved adapting traditional units for digital care, and the third inadequacies in AI application functionality and system integration. To navigate around these barriers, the leaders took steps to (1) address inexperience and fear and reduce professional scepticism, (2) align implementation with digital maturity and guide patients towards digital care, and (3) refine and improve the AI application and adapt to the current state of AI application development. CONCLUSION The study provides valuable empirical insights into the implementation of AI for automating medical history-taking and triage in primary care as experienced by healthcare leaders. It identifies the barriers to this implementation and how healthcare leaders aligned their actions to overcome them. While progress was evident in overcoming professional-related and organizational-related barriers, unresolved technical complexities highlight the importance of AI implementation strategies that consider how leaders handle AI implementation in situ based on practical wisdom and tacit understanding. This underscores the necessity of a holistic approach for the successful implementation of AI in healthcare.
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Affiliation(s)
- Elin Siira
- School of Health and Welfare, Halmstad University, Box 823, Halmstad, 301 18, Sweden
| | - Daniel Tyskbo
- School of Health and Welfare, Halmstad University, Box 823, Halmstad, 301 18, Sweden
| | - Jens Nygren
- School of Health and Welfare, Halmstad University, Box 823, Halmstad, 301 18, Sweden.
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11
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Chance EA, Florence D, Sardi Abdoul I. The effectiveness of checklists and error reporting systems in enhancing patient safety and reducing medical errors in hospital settings: A narrative review. Int J Nurs Sci 2024; 11:387-398. [PMID: 39156684 PMCID: PMC11329062 DOI: 10.1016/j.ijnss.2024.06.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 05/10/2024] [Accepted: 06/06/2024] [Indexed: 08/20/2024] Open
Abstract
Objectives This narrative review aimed to explore the impact of checklists and error reporting systems on hospital patient safety and medical errors. Methods A systematic search of academic databases from 2013 to 2023 was conducted, and peer-reviewed studies meeting inclusion criteria were assessed for methodological rigor. The review highlights evidence supporting the efficacy of checklists in reducing medication errors, surgical complications, and other adverse events. Error reporting systems foster transparency, encouraging professionals to report incidents and identify systemic vulnerabilities. Results Checklists and error reporting systems are interconnected. Interprofessional collaboration is emphasized in checklist implementation. In this review, limitations arise due to the different methodologies used in the articles and potential publication bias. In addition, language restrictions may exclude valuable non-English research. While positive impacts are evident, success depends on organizational culture and resources. Conclusions This review contributes to patient safety knowledge by examining the relevant literature, emphasizing the importance of interventions, and calling for further research into their effectiveness across diverse healthcare and cultural settings. Understanding these dynamics is crucial for healthcare providers to optimize patient safety outcomes.
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Khan M, Banerjee S, Muskawad S, Maity R, Chowdhury SR, Ejaz R, Kuuzie E, Satnarine T. The Impact of Artificial Intelligence on Allergy Diagnosis and Treatment. Curr Allergy Asthma Rep 2024; 24:361-372. [PMID: 38954325 DOI: 10.1007/s11882-024-01152-y] [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] [Accepted: 05/19/2024] [Indexed: 07/04/2024]
Abstract
PURPOSE OF REVIEW Artificial intelligence (AI), be it neuronal networks, machine learning or deep learning, has numerous beneficial effects on healthcare systems; however, its potential applications and diagnostic capabilities for immunologic diseases have yet to be explored. Understanding AI systems can help healthcare workers better assimilate artificial intelligence into their practice and unravel its potential in diagnostics, clinical research, and disease management. RECENT FINDINGS We reviewed recent advancements in AI systems and their integration in healthcare systems, along with their potential benefits in the diagnosis and management of diseases. We explored machine learning as employed in allergy diagnosis and its learning patterns from patient datasets, as well as the possible advantages of using AI in the field of research related to allergic reactions and even remote monitoring. Considering the ethical challenges and privacy concerns raised by clinicians and patients with regard to integrating AI in healthcare, we explored the new guidelines adapted by regulatory bodies. Despite these challenges, AI appears to have been successfully incorporated into various healthcare systems and is providing patient-centered solutions while simultaneously assisting healthcare workers. Artificial intelligence offers new hope in the field of immunologic disease diagnosis, monitoring, and management and thus has the potential to revolutionize healthcare systems.
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Affiliation(s)
- Maham Khan
- Fatima Jinnah Medical University, Lahore, Pakistan.
| | | | | | - Rick Maity
- Institute of Post Graduate Medical Education and Research, Kolkata, West Bengal, India
| | | | - Rida Ejaz
- Shifa College of Medicine, Islamabad, Pakistan
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13
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Alami H, Lehoux P, Papoutsi C, Shaw SE, Fleet R, Fortin JP. Understanding the integration of artificial intelligence in healthcare organisations and systems through the NASSS framework: a qualitative study in a leading Canadian academic centre. BMC Health Serv Res 2024; 24:701. [PMID: 38831298 PMCID: PMC11149257 DOI: 10.1186/s12913-024-11112-x] [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: 02/03/2023] [Accepted: 05/14/2024] [Indexed: 06/05/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) technologies are expected to "revolutionise" healthcare. However, despite their promises, their integration within healthcare organisations and systems remains limited. The objective of this study is to explore and understand the systemic challenges and implications of their integration in a leading Canadian academic hospital. METHODS Semi-structured interviews were conducted with 29 stakeholders concerned by the integration of a large set of AI technologies within the organisation (e.g., managers, clinicians, researchers, patients, technology providers). Data were collected and analysed using the Non-Adoption, Abandonment, Scale-up, Spread, Sustainability (NASSS) framework. RESULTS Among enabling factors and conditions, our findings highlight: a supportive organisational culture and leadership leading to a coherent organisational innovation narrative; mutual trust and transparent communication between senior management and frontline teams; the presence of champions, translators, and boundary spanners for AI able to build bridges and trust; and the capacity to attract technical and clinical talents and expertise. Constraints and barriers include: contrasting definitions of the value of AI technologies and ways to measure such value; lack of real-life and context-based evidence; varying patients' digital and health literacy capacities; misalignments between organisational dynamics, clinical and administrative processes, infrastructures, and AI technologies; lack of funding mechanisms covering the implementation, adaptation, and expertise required; challenges arising from practice change, new expertise development, and professional identities; lack of official professional, reimbursement, and insurance guidelines; lack of pre- and post-market approval legal and governance frameworks; diversity of the business and financing models for AI technologies; and misalignments between investors' priorities and the needs and expectations of healthcare organisations and systems. CONCLUSION Thanks to the multidimensional NASSS framework, this study provides original insights and a detailed learning base for analysing AI technologies in healthcare from a thorough socio-technical perspective. Our findings highlight the importance of considering the complexity characterising healthcare organisations and systems in current efforts to introduce AI technologies within clinical routines. This study adds to the existing literature and can inform decision-making towards a judicious, responsible, and sustainable integration of these technologies in healthcare organisations and systems.
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Affiliation(s)
- Hassane Alami
- Department of Health Management, Evaluation and Policy, School of Public Health, University of Montreal, P.O. Box 6128, Branch Centre-Ville, Montreal, QC, H3C 3J7, Canada.
- Center for Public Health Research of the University of Montreal, Montreal, QC, Canada.
- Institute for Data Valorization (IVADO), Montreal, QC, Canada.
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK.
| | - Pascale Lehoux
- Department of Health Management, Evaluation and Policy, School of Public Health, University of Montreal, P.O. Box 6128, Branch Centre-Ville, Montreal, QC, H3C 3J7, Canada
- Center for Public Health Research of the University of Montreal, Montreal, QC, Canada
| | - Chrysanthi Papoutsi
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Sara E Shaw
- Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK
| | - Richard Fleet
- Faculty of Medicine, Laval University, Quebec, QC, Canada
- VITAM Research Centre on Sustainable Health, Faculty of Medicine, Laval University, Quebec, QC, Canada
| | - Jean-Paul Fortin
- Faculty of Medicine, Laval University, Quebec, QC, Canada
- VITAM Research Centre on Sustainable Health, Faculty of Medicine, Laval University, Quebec, QC, Canada
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14
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Kommuru S, Adekunle F, Niño S, Arefin S, Thalvayapati SP, Kuriakose D, Ahmadi Y, Vinyak S, Nazir Z. Role of Artificial Intelligence in the Diagnosis of Gastroesophageal Reflux Disease. Cureus 2024; 16:e62206. [PMID: 39006681 PMCID: PMC11240074 DOI: 10.7759/cureus.62206] [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] [Accepted: 06/09/2024] [Indexed: 07/16/2024] Open
Abstract
Gastroesophageal reflux disease (GERD) is a disorder that usually presents with heartburn. GERD is diagnosed clinically, but most patients are misdiagnosed due to atypical presentations. The increased use of artificial intelligence (AI) in healthcare has provided multiple ways of diagnosing and treating patients accurately. In this review, multiple studies in which AI models were used to diagnose GERD are discussed. According to the studies, using AI models helped to diagnose GERD in patients accurately. AI, although considered one of the most potent emerging aspects of medicine with its accuracy in patient diagnosis, presents limitations of its own, which explains why healthcare providers may hesitate to use AI in patient care. The challenges and limitations should be addressed before AI is fully incorporated into the healthcare system.
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Affiliation(s)
- Sravani Kommuru
- Medical School, Dr. Pinnamaneni Siddhartha Institute of Medical Sciences & Research Foundation, Vijayawada, IND
| | - Faith Adekunle
- Medical School, American University of the Carribbean, Cupecoy, SXM
| | - Santiago Niño
- Surgery, Colegio Mayor de Nuestra Señora del Rosario, Bogota, COL
| | - Shamsul Arefin
- Internal Medicine, Nottingham University Hospitals NHS Trust, Nottingham, GBR
| | | | - Dona Kuriakose
- Internal Medicine, Petre Shotadze Tbilisi Medical Academy, Tbilisi, GEO
| | - Yasmin Ahmadi
- Medical School, Royal College of Surgeons in Ireland - Medical University of Bahrain, Busaiteen, BHR
| | - Suprada Vinyak
- Internal Medicine, Wellmont Health System/Norton Community Hospital, Norton, USA
| | - Zahra Nazir
- Internal Medicine, Combined Military Hospital, Quetta, PAK
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15
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Harishbhai Tilala M, Kumar Chenchala P, Choppadandi A, Kaur J, Naguri S, Saoji R, Devaguptapu B. Ethical Considerations in the Use of Artificial Intelligence and Machine Learning in Health Care: A Comprehensive Review. Cureus 2024; 16:e62443. [PMID: 39011215 PMCID: PMC11249277 DOI: 10.7759/cureus.62443] [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: 05/17/2024] [Accepted: 06/15/2024] [Indexed: 07/17/2024] Open
Abstract
Artificial intelligence (AI) and machine learning (ML) technologies are revolutionizing health care by offering unprecedented opportunities to enhance patient care, optimize clinical workflows, and advance medical research. However, the integration of AI and ML into healthcare systems raises significant ethical considerations that must be carefully addressed to ensure responsible and equitable deployment. This comprehensive review explored the multifaceted ethical considerations surrounding the use of AI and ML in health care, including privacy and data security, algorithmic bias, transparency, clinical validation, and professional responsibility. By critically examining these ethical dimensions, stakeholders can navigate the ethical complexities of AI and ML integration in health care, while safeguarding patient welfare and upholding ethical principles. By embracing ethical best practices and fostering collaboration across interdisciplinary teams, the healthcare community can harness the full potential of AI and ML technologies to usher in a new era of personalized data-driven health care that prioritizes patient well-being and equity.
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Affiliation(s)
| | | | | | - Jagbir Kaur
- Program Management, Independent Researcher, West Orange, USA
| | | | - Rahul Saoji
- Data Analytics, Independent Researcher, Dallas, USA
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16
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Al-Naser Y. How medical radiation technologists can foster equity, diversity, and inclusion through artificial intelligence in radiology. J Med Imaging Radiat Sci 2024:101436. [PMID: 38825546 DOI: 10.1016/j.jmir.2024.101436] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 05/14/2024] [Accepted: 05/15/2024] [Indexed: 06/04/2024]
Affiliation(s)
- Yousif Al-Naser
- Medical Radiation Sciences, McMaster University, Hamilton, ON, Canada; Department of Diagnostic Imaging, Trillium Health Partners, Mississauga, ON, Canada.
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17
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Alhuwaydi AM. Exploring the Role of Artificial Intelligence in Mental Healthcare: Current Trends and Future Directions - A Narrative Review for a Comprehensive Insight. Risk Manag Healthc Policy 2024; 17:1339-1348. [PMID: 38799612 PMCID: PMC11127648 DOI: 10.2147/rmhp.s461562] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Accepted: 05/10/2024] [Indexed: 05/29/2024] Open
Abstract
Mental health is an essential component of the health and well-being of a person and community, and it is critical for the individual, society, and socio-economic development of any country. Mental healthcare is currently in the health sector transformation era, with emerging technologies such as artificial intelligence (AI) reshaping the screening, diagnosis, and treatment modalities of psychiatric illnesses. The present narrative review is aimed at discussing the current landscape and the role of AI in mental healthcare, including screening, diagnosis, and treatment. Furthermore, this review attempted to highlight the key challenges, limitations, and prospects of AI in providing mental healthcare based on existing works of literature. The literature search for this narrative review was obtained from PubMed, Saudi Digital Library (SDL), Google Scholar, Web of Science, and IEEE Xplore, and we included only English-language articles published in the last five years. Keywords used in combination with Boolean operators ("AND" and "OR") were the following: "Artificial intelligence", "Machine learning", Deep learning", "Early diagnosis", "Treatment", "interventions", "ethical consideration", and "mental Healthcare". Our literature review revealed that, equipped with predictive analytics capabilities, AI can improve treatment planning by predicting an individual's response to various interventions. Predictive analytics, which uses historical data to formulate preventative interventions, aligns with the move toward individualized and preventive mental healthcare. In the screening and diagnostic domains, a subset of AI, such as machine learning and deep learning, has been proven to analyze various mental health data sets and predict the patterns associated with various mental health problems. However, limited studies have evaluated the collaboration between healthcare professionals and AI in delivering mental healthcare, as these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches. Ethical issues, cybersecurity, a lack of data analytics diversity, cultural sensitivity, and language barriers remain concerns for implementing this futuristic approach in mental healthcare. Considering these sensitive problems require empathy, human connections, and holistic, personalized, and multidisciplinary approaches, it is imperative to explore these aspects. Therefore, future comparative trials with larger sample sizes and data sets are warranted to evaluate different AI models used in mental healthcare across regions to fill the existing knowledge gaps.
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Affiliation(s)
- Ahmed M Alhuwaydi
- Department of Internal Medicine, Division of Psychiatry, College of Medicine, Jouf University, Sakaka, Saudi Arabia
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18
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Aljabali AAA, Obeid MA, El-Tanani M, Mishra V, Mishra Y, Tambuwala MM. Precision epidemiology at the nexus of mathematics and nanotechnology: Unraveling the dance of viral dynamics. Gene 2024; 905:148174. [PMID: 38242374 DOI: 10.1016/j.gene.2024.148174] [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: 11/28/2023] [Revised: 01/10/2024] [Accepted: 01/16/2024] [Indexed: 01/21/2024]
Abstract
The intersection of mathematical modeling, nanotechnology, and epidemiology marks a paradigm shift in our battle against infectious diseases, aligning with the focus of the journal on the regulation, expression, function, and evolution of genes in diverse biological contexts. This exploration navigates the intricate dance of viral transmission dynamics, highlighting mathematical models as dual tools of insight and precision instruments, a theme relevant to the diverse sections of Gene. In the context of virology, ethical considerations loom large, necessitating robust frameworks to protect individual rights, an aspect essential in infectious disease research. Global collaboration emerges as a critical pillar in our response to emerging infectious diseases, fortified by the predictive prowess of mathematical models enriched by nanotechnology. The synergy of interdisciplinary collaboration, training the next generation to bridge mathematical rigor, biology, and epidemiology, promises accelerated discoveries and robust models that account for real-world complexities, fostering innovation and exploration in the field. In this intricate review, mathematical modeling in viral transmission dynamics and epidemiology serves as a guiding beacon, illuminating the path toward precision interventions, global preparedness, and the collective endeavor to safeguard human health, resonating with the aim of advancing knowledge in gene regulation and expression.
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Affiliation(s)
- Alaa A A Aljabali
- Faculty of Pharmacy, Department of Pharmaceutics & Pharmaceutical Technology, Yarmouk University, Irbid 21163, Jordan.
| | - Mohammad A Obeid
- Faculty of Pharmacy, Department of Pharmaceutics & Pharmaceutical Technology, Yarmouk University, Irbid 21163, Jordan
| | - Mohamed El-Tanani
- College of Pharmacy, Ras Al Khaimah Medical and Health Sciences University, Ras Al Khaimah, United Arab Emirates.
| | - Vijay Mishra
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara, Punjab 144411, India
| | - Yachana Mishra
- School of Bioengineering and Biosciences, Lovely Professional University, Phagwara, Punjab 144411, India
| | - Murtaza M Tambuwala
- Lincoln Medical School, University of Lincoln, Brayford Pool Campus, Lincoln LN6 7TS, United Kingdom.
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Khan SD, Hoodbhoy Z, Raja MHR, Kim JY, Hogg HDJ, Manji AAA, Gulamali F, Hasan A, Shaikh A, Tajuddin S, Khan NS, Patel MR, Balu S, Samad Z, Sendak MP. Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review. PLOS DIGITAL HEALTH 2024; 3:e0000514. [PMID: 38809946 PMCID: PMC11135672 DOI: 10.1371/journal.pdig.0000514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 04/18/2024] [Indexed: 05/31/2024]
Abstract
Research on the applications of artificial intelligence (AI) tools in medicine has increased exponentially over the last few years but its implementation in clinical practice has not seen a commensurate increase with a lack of consensus on implementing and maintaining such tools. This systematic review aims to summarize frameworks focusing on procuring, implementing, monitoring, and evaluating AI tools in clinical practice. A comprehensive literature search, following PRSIMA guidelines was performed on MEDLINE, Wiley Cochrane, Scopus, and EBSCO databases, to identify and include articles recommending practices, frameworks or guidelines for AI procurement, integration, monitoring, and evaluation. From the included articles, data regarding study aim, use of a framework, rationale of the framework, details regarding AI implementation involving procurement, integration, monitoring, and evaluation were extracted. The extracted details were then mapped on to the Donabedian Plan, Do, Study, Act cycle domains. The search yielded 17,537 unique articles, out of which 47 were evaluated for inclusion based on their full texts and 25 articles were included in the review. Common themes extracted included transparency, feasibility of operation within existing workflows, integrating into existing workflows, validation of the tool using predefined performance indicators and improving the algorithm and/or adjusting the tool to improve performance. Among the four domains (Plan, Do, Study, Act) the most common domain was Plan (84%, n = 21), followed by Study (60%, n = 15), Do (52%, n = 13), & Act (24%, n = 6). Among 172 authors, only 1 (0.6%) was from a low-income country (LIC) and 2 (1.2%) were from lower-middle-income countries (LMICs). Healthcare professionals cite the implementation of AI tools within clinical settings as challenging owing to low levels of evidence focusing on integration in the Do and Act domains. The current healthcare AI landscape calls for increased data sharing and knowledge translation to facilitate common goals and reap maximum clinical benefit.
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Affiliation(s)
- Sarim Dawar Khan
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Zahra Hoodbhoy
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
- Department of Paediatrics and Child Health, Aga Khan University, Karachi, Pakistan
| | | | - Jee Young Kim
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Henry David Jeffry Hogg
- Population Health Science Institute, Newcastle University, Newcastle upon Tyne, United Kingdom
- Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne, United Kingdom
- Moorfields Eye Hospital NHS Foundation Trust, London, United Kingdom
| | - Afshan Anwar Ali Manji
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Freya Gulamali
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Alifia Hasan
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Asim Shaikh
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Salma Tajuddin
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Nida Saddaf Khan
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Manesh R. Patel
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, United States
- Division of Cardiology, Duke University School of Medicine, Durham, North Carolina, United States
| | - Suresh Balu
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
| | - Zainab Samad
- CITRIC Health Data Science Centre, Department of Medicine, Aga Khan University, Karachi, Pakistan
- Department of Medicine, Aga Khan University, Karachi, Pakistan
| | - Mark P. Sendak
- Duke Institute for Health Innovation, Duke University School of Medicine, Durham, North Carolina, United States
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20
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Miles TJ, Ghanta RK. Machine learning in cardiac surgery: a narrative review. J Thorac Dis 2024; 16:2644-2653. [PMID: 38738250 PMCID: PMC11087616 DOI: 10.21037/jtd-23-1659] [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: 10/31/2023] [Accepted: 03/15/2024] [Indexed: 05/14/2024]
Abstract
Background and Objective Machine learning (ML) is increasingly being utilized to provide data driven solutions to challenges in medicine. Within the field of cardiac surgery, ML methods have been employed as risk stratification tools to predict a variety of operative outcomes. However, the clinical utility of ML in this domain is unclear. The aim of this review is to provide an overview of ML in cardiac surgery, particularly with regards to its utility in predictive analytics and implications for use in clinical decision support. Methods We performed a narrative review of relevant articles indexed in PubMed since 2000 using the MeSH terms "Machine Learning", "Supervised Machine Learning", "Deep Learning", or "Artificial Intelligence" and "Cardiovascular Surgery" or "Thoracic Surgery". Key Content and Findings ML methods have been widely used to generate pre-operative risk profiles, consistently resulting in the accurate prediction of clinical outcomes in cardiac surgery. However, improvement in predictive performance over traditional risk metrics has proven modest and current applications in the clinical setting remain limited. Conclusions Studies utilizing high volume, multidimensional data such as that derived from electronic health record (EHR) data appear to best demonstrate the advantages of ML methods. Models trained on post cardiac surgery intensive care unit data demonstrate excellent predictive performance and may provide greater clinical utility if incorporated as clinical decision support tools. Further development of ML models and their integration into EHR's may result in dynamic clinical decision support strategies capable of informing clinical care and improving outcomes in cardiac surgery.
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Affiliation(s)
- Travis J. Miles
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- Applied Statistics and Machine Learning for the Advancement of Surgery, Department of Surgery, Baylor College of Medicine, Houston, TX, USA
| | - Ravi K. Ghanta
- Michael E. DeBakey Department of Surgery, Baylor College of Medicine, Houston, TX, USA
- Applied Statistics and Machine Learning for the Advancement of Surgery, Department of Surgery, Baylor College of Medicine, Houston, TX, USA
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Moy S, Irannejad M, Manning SJ, Farahani M, Ahmed Y, Gao E, Prabhune R, Lorenz S, Mirza R, Klinger C. Patient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review. J Patient Cent Res Rev 2024; 11:51-62. [PMID: 38596349 PMCID: PMC11000703 DOI: 10.17294/2330-0698.2029] [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: 04/11/2024] Open
Abstract
Purpose Artificial intelligence (AI) technology is being rapidly adopted into many different branches of medicine. Although research has started to highlight the impact of AI on health care, the focus on patient perspectives of AI is scarce. This scoping review aimed to explore the literature on adult patients' perspectives on the use of an array of AI technologies in the health care setting for design and deployment. Methods This scoping review followed Arksey and O'Malley's framework and Preferred Reporting Items for Systematic Reviews and Meta-Analysis for Scoping Reviews (PRISMA-ScR). To evaluate patient perspectives, we conducted a comprehensive literature search using eight interdisciplinary electronic databases, including grey literature. Articles published from 2015 to 2022 that focused on patient views regarding AI technology in health care were included. Thematic analysis was performed on the extracted articles. Results Of the 10,571 imported studies, 37 articles were included and extracted. From the 33 peer-reviewed and 4 grey literature articles, the following themes on AI emerged: (i) Patient attitudes, (ii) Influences on patient attitudes, (iii) Considerations for design, and (iv) Considerations for use. Conclusions Patients are key stakeholders essential to the uptake of AI in health care. The findings indicate that patients' needs and expectations are not fully considered in the application of AI in health care. Therefore, there is a need for patient voices in the development of AI in health care.
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Affiliation(s)
- Sally Moy
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Mona Irannejad
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | | | - Mehrdad Farahani
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Yomna Ahmed
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Ellis Gao
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Radhika Prabhune
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Suzan Lorenz
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Raza Mirza
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Christopher Klinger
- Translational Research Program, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada
- National Initiative for the Care of the Elderly, Toronto, Canada
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22
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Wimbarti S, Kairupan BHR, Tallei TE. Critical review of self-diagnosis of mental health conditions using artificial intelligence. Int J Ment Health Nurs 2024; 33:344-358. [PMID: 38345132 DOI: 10.1111/inm.13303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 01/26/2024] [Accepted: 01/30/2024] [Indexed: 03/10/2024]
Abstract
The advent of artificial intelligence (AI) has revolutionised various aspects of our lives, including mental health nursing. AI-driven tools and applications have provided a convenient and accessible means for individuals to assess their mental well-being within the confines of their homes. Nonetheless, the widespread trend of self-diagnosing mental health conditions through AI poses considerable risks. This review article examines the perils associated with relying on AI for self-diagnosis in mental health, highlighting the constraints and possible adverse outcomes that can arise from such practices. It delves into the ethical, psychological, and social implications, underscoring the vital role of mental health professionals, including psychologists, psychiatrists, and nursing specialists, in providing professional assistance and guidance. This article aims to highlight the importance of seeking professional assistance and guidance in addressing mental health concerns, especially in the era of AI-driven self-diagnosis.
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Affiliation(s)
- Supra Wimbarti
- Faculty of Psychology, Universitas Gadjah Mada, Yogyakarta, Indonesia
| | - B H Ralph Kairupan
- Department of Psychiatry, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
| | - Trina Ekawati Tallei
- Department of Biology, Faculty of Mathematics and Natural Sciences, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
- Department of Biology, Faculty of Medicine, Sam Ratulangi University, Manado, North Sulawesi, Indonesia
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23
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Stogiannos N, O'Regan T, Scurr E, Litosseliti L, Pogose M, Harvey H, Kumar A, Malik R, Barnes A, McEntee MF, Malamateniou C. AI implementation in the UK landscape: Knowledge of AI governance, perceived challenges and opportunities, and ways forward for radiographers. Radiography (Lond) 2024; 30:612-621. [PMID: 38325103 DOI: 10.1016/j.radi.2024.01.019] [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: 01/04/2024] [Accepted: 01/26/2024] [Indexed: 02/09/2024]
Abstract
INTRODUCTION Despite the rapid increase of AI-enabled applications deployed in clinical practice, many challenges exist around AI implementation, including the clarity of governance frameworks, usability of validation of AI models, and customisation of training for radiographers. This study aimed to explore the perceptions of diagnostic and therapeutic radiographers, with existing theoretical and/or practical knowledge of AI, on issues of relevance to the field, such as AI implementation, including knowledge of AI governance and procurement, perceptions about enablers and challenges and future priorities for AI adoption. METHODS An online survey was designed and distributed to UK-based qualified radiographers who work in medical imaging and/or radiotherapy and have some previous theoretical and/or practical knowledge of working with AI. Participants were recruited through the researchers' professional networks on social media with support from the AI advisory group of the Society and College of Radiographers. Survey questions related to AI training/education, knowledge of AI governance frameworks, data privacy procedures, AI implementation considerations, and priorities for AI adoption. Descriptive statistics were employed to analyse the data, and chi-square tests were used to explore significant relationships between variables. RESULTS In total, 88 valid responses were received. Most radiographers (56.6 %) had not received any AI-related training. Also, although approximately 63 % of them used an evaluation framework to assess AI models' performance before implementation, many (36.9 %) were still unsure about suitable evaluation methods. Radiographers requested clearer guidance on AI governance, ample time to implement AI in their practice safely, adequate funding, effective leadership, and targeted support from AI champions. AI training, robust governance frameworks, and patient and public involvement were seen as priorities for the successful implementation of AI by radiographers. CONCLUSION AI implementation is progressing within radiography, but without customised training, clearer governance, key stakeholder engagement and suitable new roles created, it will be hard to harness its benefits and minimise related risks. IMPLICATIONS FOR PRACTICE The results of this study highlight some of the priorities and challenges for radiographers in relation to AI adoption, namely the need for developing robust AI governance frameworks and providing optimal AI training.
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Affiliation(s)
- N Stogiannos
- Division of Midwifery & Radiography, City, University of London, UK; Medical Imaging Department, Corfu General Hospital, Greece.
| | - T O'Regan
- The Society and College of Radiographers, London, UK.
| | - E Scurr
- The Royal Marsden NHS Foundation Trust, UK.
| | - L Litosseliti
- School of Health & Psychological Sciences, City, University of London, UK.
| | - M Pogose
- Quality Assurance and Regulatory Affairs, Hardian Health, UK.
| | | | - A Kumar
- Frimley Health NHS Foundation Trust, UK.
| | - R Malik
- Bolton NHS Foundation Trust, UK.
| | - A Barnes
- King's Technology Evaluation Centre (KiTEC), School of Biomedical Engineering & Imaging Science, King's College London, UK.
| | - M F McEntee
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Ireland.
| | - C Malamateniou
- Division of Midwifery & Radiography, City, University of London, UK; Society and College of Radiographers AI Advisory Group, London, UK; European Society of Medical Imaging Informatics, Vienna, Austria; European Federation of Radiographer Societies, Cumieira, Portugal.
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Rony MKK, Kayesh I, Bala SD, Akter F, Parvin MR. Artificial intelligence in future nursing care: Exploring perspectives of nursing professionals - A descriptive qualitative study. Heliyon 2024; 10:e25718. [PMID: 38370178 PMCID: PMC10869862 DOI: 10.1016/j.heliyon.2024.e25718] [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: 11/21/2023] [Revised: 01/29/2024] [Accepted: 02/01/2024] [Indexed: 02/20/2024] Open
Abstract
Background The healthcare landscape is rapidly evolving, with artificial intelligence (AI) emerging as a transformative force. In this context, understanding the viewpoints of nursing professionals regarding the integration of AI in future nursing care is crucial. Aims This study aimed to provide insights into the perceptions of nursing professionals regarding the role of AI in shaping the future of healthcare. Methods A cohort of 23 nursing professionals was recruited between April 7, 2023, and May 4, 2023, for this study. Employing a thematic analysis approach, qualitative data from interviews with nursing professionals were analyzed. Verbatim transcripts underwent rigorous coding, and these codes were organized into themes through constant comparative analysis. The themes were refined and developed through the grouping of related codes, ensuring an authentic representation of participants' viewpoints. Results After careful data analysis, ten key themes emerged including: (I) Perceptions of AI readiness; (II) Benefits and concerns; (III) Enhanced patient outcomes; (IV) Collaboration and workflow; (V) Human-tech balance: (VI) Training and skill development; (VII) Ethical and legal considerations; (VIII) AI implementation barriers; (IX) Patient-nurse relationships; (X) Future vision and adaptation. Conclusion This study provides valuable insights into nursing professionals' perspectives on the integration of AI in future nursing care. It highlights their enthusiasm for AI's potential benefits while emphasizing the importance of ethical and compassionate nursing practice. The findings underscore the need for comprehensive training programs to equip nursing professionals with the skills necessary for successful AI integration. Ultimately, this research contributes to the ongoing discourse on the role of AI in nursing, paving the way for a future where innovative technologies complement and enhance the delivery of patient-centered care.
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Affiliation(s)
- Moustaq Karim Khan Rony
- Master of Public Health, Bangladesh Open University, Gazipur, Bangladesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
| | - Ibne Kayesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
| | - Shuvashish Das Bala
- Associate Professor, International University of Business Agriculture and Technology, Dhaka, Bangladesh
| | - Fazila Akter
- Dhaka Nursing College, affiliated with the University of Dhaka, Bangladesh
| | - Mst Rina Parvin
- Afns Major at Bangladesh Army, Combined Military Hospital, Dhaka, Bangladesh
- School of Medical Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
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Carrera A, Manetti S, Lettieri E. Rewiring care delivery through Digital Therapeutics (DTx): a machine learning-enhanced assessment and development (M-LEAD) framework. BMC Health Serv Res 2024; 24:237. [PMID: 38395905 PMCID: PMC10885456 DOI: 10.1186/s12913-024-10702-z] [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: 12/04/2023] [Accepted: 02/09/2024] [Indexed: 02/25/2024] Open
Abstract
BACKGROUND Digital transformation has sparked profound change in the healthcare sector through the development of innovative digital technologies. Digital Therapeutics offer an innovative approach to disease management and treatment. Care delivery is increasingly patient-centered, data-driven, and based on real-time information. These technological innovations can lead to better patient outcomes and support for healthcare professionals, also considering resource scarcity. As these digital technologies continue to evolve, the healthcare field must be ready to integrate them into processes to take advantage of their benefits. This study aims to develop a framework for the development and assessment of Digital Therapeutics. METHODS The study was conducted relying on a mixed methodology. 338 studies about Digital Therapeutics resulting from a systematic literature review were analyzed using descriptive statistics through RStudio. Machine learning algorithms were applied to analyze variables and find patterns in the data. The results of these analytical analyses were summarized in a framework qualitatively tested and validated through expert opinion elicitation. RESULTS The research provides M-LEAD, a Machine Learning-Enhanced Assessment and Development framework that recommends best practices for developing and assessing Digital Therapeutics. The framework takes as input Digital Therapeutics characteristics, regulatory aspects, study purpose, and assessment domains. The framework produces as outputs recommendations to design the Digital Therapeutics study characteristics. CONCLUSIONS The framework constitutes the first step toward standardized guidelines for the development and assessment of Digital Therapeutics. The results may support manufacturers and inform decision-makers of the relevant results of the Digital Therapeutics assessment.
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Lam C, Wong YL, Tang Z, Hu X, Nguyen TX, Yang D, Zhang S, Ding J, Szeto SKH, Ran AR, Cheung CY. Performance of Artificial Intelligence in Detecting Diabetic Macular Edema From Fundus Photography and Optical Coherence Tomography Images: A Systematic Review and Meta-analysis. Diabetes Care 2024; 47:304-319. [PMID: 38241500 DOI: 10.2337/dc23-0993] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 11/01/2023] [Indexed: 01/21/2024]
Abstract
BACKGROUND Diabetic macular edema (DME) is the leading cause of vision loss in people with diabetes. Application of artificial intelligence (AI) in interpreting fundus photography (FP) and optical coherence tomography (OCT) images allows prompt detection and intervention. PURPOSE To evaluate the performance of AI in detecting DME from FP or OCT images and identify potential factors affecting model performances. DATA SOURCES We searched seven electronic libraries up to 12 February 2023. STUDY SELECTION We included studies using AI to detect DME from FP or OCT images. DATA EXTRACTION We extracted study characteristics and performance parameters. DATA SYNTHESIS Fifty-three studies were included in the meta-analysis. FP-based algorithms of 25 studies yielded pooled area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity of 0.964, 92.6%, and 91.1%, respectively. OCT-based algorithms of 28 studies yielded pooled AUROC, sensitivity, and specificity of 0.985, 95.9%, and 97.9%, respectively. Potential factors improving model performance included deep learning techniques, larger size, and more diversity in training data sets. Models demonstrated better performance when validated internally than externally, and those trained with multiple data sets showed better results upon external validation. LIMITATIONS Analyses were limited by unstandardized algorithm outcomes and insufficient data in patient demographics, OCT volumetric scans, and external validation. CONCLUSIONS This meta-analysis demonstrates satisfactory performance of AI in detecting DME from FP or OCT images. External validation is warranted for future studies to evaluate model generalizability. Further investigations may estimate optimal sample size, effect of class balance, patient demographics, and additional benefits of OCT volumetric scans.
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Affiliation(s)
- Ching Lam
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yiu Lun Wong
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Ziqi Tang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Xiaoyan Hu
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Truong X Nguyen
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Dawei Yang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Shuyi Zhang
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | | | - Simon K H Szeto
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
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Royapuram Parthasarathy P, Patil SR, Dawasaz AA, Hamid Baig FA, Karobari MI. Unlocking the Potential: Investigating Dental Practitioners' Willingness to Embrace Artificial Intelligence in Dental Practice. Cureus 2024; 16:e55107. [PMID: 38558604 PMCID: PMC10979078 DOI: 10.7759/cureus.55107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/25/2024] [Indexed: 04/04/2024] Open
Abstract
BACKGROUND Artificial intelligence (AI) holds significant promise for transforming healthcare delivery, including dentistry. However, the successful integration of AI into dental practice necessitates an understanding of dental professionals' perspectives, attitudes, and readiness to adopt AI technology. This study aimed to explore dental professionals' perceptions, attitudes, and practices regarding AI adoption in dentistry. METHODS This cross-sectional study was conducted among 256 dental professionals using an online questionnaire. Participants were assessed for familiarity with AI technology, perceived barriers to adoption, attitudes towards AI, current usage patterns, and factors influencing adoption decisions. Data are analysed using descriptive statistics, including frequencies, percentages, means, and standard deviations. Inferential statistics, such as chi-square tests and regression analysis, were employed to examine associations between variables and identify predictors of AI adoption in dentistry. RESULTS The study surveyed 256 dental professionals from various regions across India, primarily aged 30 to 50 years (mean age: 42.6), with a nearly equal gender split (male: 48.4%, female: 51.6%) and high educational attainment (67.8% with master's or doctoral degrees). Private practices were predominant (56.3%). The diagnostic algorithms and treatment planning software were well known (77.3% and 70.3% familiarity, respectively). Technical concerns (average score: 3.82 ± 0.68) were the main barriers to AI adoption, followed by financial considerations (average score: 3.45 ± 0.72), ethical and legal issues (average score: 3.21 ± 0.65), and organizational factors (average score: 3.67 ± 0.71). Despite these concerns, most participants had positive attitudes towards AI (70.3% agreed). Current usage varied, with diagnostic support and administrative tasks being the most common (44.5% and 82.8% usage, respectively). Perceived utility (average score: 4.12 ± 0.75) and ease of use (average score: 3.98 ± 0.69) significantly influenced adoption, as identified by regression analysis (perceived utility: β = 0.342, p < 0.001; ease of use: β = 0.267, p = 0.005). CONCLUSION This study provides valuable insights into AI adoption in dentistry, highlighting the multifaceted nature of barriers and facilitators that influence dental professionals' adoption decisions. Strategies to promote AI adoption should address practical considerations, ethical concerns, and educational needs to facilitate the integration of AI technology into dental practices.
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Affiliation(s)
- Parameswari Royapuram Parthasarathy
- Centre for Global Health Research, Saveetha Medical College and Hospitals, Saveetha Institute of Medical and Technical Sciences, Chennai, IND
| | - Santosh R Patil
- Department of Oral Medicine and Radiology, Chhattisgarh Dental College and Research Institute, Rajnandgaon, IND
| | - Ali Azhar Dawasaz
- Department of Diagnostic Dental Sciences, College of Dentistry, King Khalid University, Abha, SAU
| | - Fawaz Abdul Hamid Baig
- Department of Oral and Maxillofacial Surgery, College of Dentistry, King Khalid University, Abha, SAU
| | - Mohmed Isaqali Karobari
- Dental Research Unit, Centre for Global Health Research, Saveetha Institute of Medical and Technical Sciences, Chennai, IND
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28
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Tripathi S, Tabari A, Mansur A, Dabbara H, Bridge CP, Daye D. From Machine Learning to Patient Outcomes: A Comprehensive Review of AI in Pancreatic Cancer. Diagnostics (Basel) 2024; 14:174. [PMID: 38248051 PMCID: PMC10814554 DOI: 10.3390/diagnostics14020174] [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: 09/19/2023] [Revised: 12/28/2023] [Accepted: 12/29/2023] [Indexed: 01/23/2024] Open
Abstract
Pancreatic cancer is a highly aggressive and difficult-to-detect cancer with a poor prognosis. Late diagnosis is common due to a lack of early symptoms, specific markers, and the challenging location of the pancreas. Imaging technologies have improved diagnosis, but there is still room for improvement in standardizing guidelines. Biopsies and histopathological analysis are challenging due to tumor heterogeneity. Artificial Intelligence (AI) revolutionizes healthcare by improving diagnosis, treatment, and patient care. AI algorithms can analyze medical images with precision, aiding in early disease detection. AI also plays a role in personalized medicine by analyzing patient data to tailor treatment plans. It streamlines administrative tasks, such as medical coding and documentation, and provides patient assistance through AI chatbots. However, challenges include data privacy, security, and ethical considerations. This review article focuses on the potential of AI in transforming pancreatic cancer care, offering improved diagnostics, personalized treatments, and operational efficiency, leading to better patient outcomes.
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Affiliation(s)
- Satvik Tripathi
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Harvard Medical School, Boston, MA 02115, USA
| | - Arian Mansur
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Harvard Medical School, Boston, MA 02115, USA
| | - Harika Dabbara
- Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118, USA;
| | - Christopher P. Bridge
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA; (S.T.); (A.T.); (A.M.); (C.P.B.)
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA 02129, USA
- Harvard Medical School, Boston, MA 02115, USA
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29
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Chae A, Yao MS, Sagreiya H, Goldberg AD, Chatterjee N, MacLean MT, Duda J, Elahi A, Borthakur A, Ritchie MD, Rader D, Kahn CE, Witschey WR, Gee JC. Strategies for Implementing Machine Learning Algorithms in the Clinical Practice of Radiology. Radiology 2024; 310:e223170. [PMID: 38259208 PMCID: PMC10831483 DOI: 10.1148/radiol.223170] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 08/24/2023] [Accepted: 08/29/2023] [Indexed: 01/24/2024]
Abstract
Despite recent advancements in machine learning (ML) applications in health care, there have been few benefits and improvements to clinical medicine in the hospital setting. To facilitate clinical adaptation of methods in ML, this review proposes a standardized framework for the step-by-step implementation of artificial intelligence into the clinical practice of radiology that focuses on three key components: problem identification, stakeholder alignment, and pipeline integration. A review of the recent literature and empirical evidence in radiologic imaging applications justifies this approach and offers a discussion on structuring implementation efforts to help other hospital practices leverage ML to improve patient care. Clinical trial registration no. 04242667 © RSNA, 2024 Supplemental material is available for this article.
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Affiliation(s)
| | | | - Hersh Sagreiya
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Ari D. Goldberg
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Neil Chatterjee
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Matthew T. MacLean
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Jeffrey Duda
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Ameena Elahi
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Arijitt Borthakur
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Marylyn D. Ritchie
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Daniel Rader
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
| | - Charles E. Kahn
- From the Departments of Bioengineering (M.S.Y.), Radiology (H.S.,
N.C., M.T.M., J.D., A.B., C.E.K., W.R.W., J.C.G.), Genetics (M.D.R.), and
Medicine (D.R.), Perelman School of Medicine (A.C., M.S.Y., H.S., A.B., C.E.K.,
W.R.W., J.C.G.), University of Pennsylvania, 3400 Civic Center Blvd,
Philadelphia, PA 19104; Department of Radiology, Loyola University Medical
Center, Maywood, Ill (A.D.G.); Department of Information Services, University of
Pennsylvania, Philadelphia, Pa (A.E.); and Leonard Davis Institute of Health
Economics, University of Pennsylvania, Philadelphia, Pa (A.B.)
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O'Reilly D, McGrath J, Martin-Loeches I. Optimizing artificial intelligence in sepsis management: Opportunities in the present and looking closely to the future. JOURNAL OF INTENSIVE MEDICINE 2024; 4:34-45. [PMID: 38263963 PMCID: PMC10800769 DOI: 10.1016/j.jointm.2023.10.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 04/22/2023] [Revised: 09/29/2023] [Accepted: 10/01/2023] [Indexed: 01/25/2024]
Abstract
Sepsis remains a major challenge internationally for healthcare systems. Its incidence is rising due to poor public awareness and delays in its recognition and subsequent management. In sepsis, mortality increases with every hour left untreated. Artificial intelligence (AI) is transforming worldwide healthcare delivery at present. This review has outlined how AI can augment strategies to address this global disease burden. AI and machine learning (ML) algorithms can analyze vast quantities of increasingly complex clinical datasets from electronic medical records to assist clinicians in diagnosing and treating sepsis earlier than traditional methods. Our review highlights how these models can predict the risk of sepsis and organ failure even before it occurs. This gives providers additional time to plan and execute treatment plans, thereby avoiding increasing complications associated with delayed diagnosis of sepsis. The potential for cost savings with AI implementation is also discussed, including improving workflow efficiencies, reducing administrative costs, and improving healthcare outcomes. Despite these advantages, clinicians have been slow to adopt AI into clinical practice. Some of the limitations posed by AI solutions include the lack of diverse data sets for model building so that they are widely applicable for routine clinical use. Furthermore, the subsequent algorithms are often based on complex mathematics leading to clinician hesitancy to embrace such technologies. Finally, we highlight the need for robust political and regulatory frameworks in this area to achieve the trust and approval of clinicians and patients to implement this transformational technology.
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Affiliation(s)
- Darragh O'Reilly
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Jennifer McGrath
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
| | - Ignacio Martin-Loeches
- Department of Intensive Care Medicine, Multidisciplinary Intensive Care Research Organization (MICRO), St James’ Hospital, Dublin, Ireland
- Department of Respiratory Intensive care, Hospital Clinic, Universitat de Barcelona, IDIBAPS, CIBERES, Barcelona, Spain
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Mansour T, Bick M. How can physicians adopt AI-based applications in the United Arab Emirates to improve patient outcomes? Digit Health 2024; 10:20552076241284936. [PMID: 39351313 PMCID: PMC11440542 DOI: 10.1177/20552076241284936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2024] [Accepted: 09/02/2024] [Indexed: 10/04/2024] Open
Abstract
Objective The enabling and derailing factors for using artificial intelligence (AI)-based applications to improve patient care in the United Arab Emirates (UAE) from the physicians' perspective are investigated. Factors to accelerate the adoption of AI-based applications in the UAE are identified to aid implementation. Methods A qualitative, inductive research methodology was employed, utilizing semi-structured interviews with 12 physicians practicing in the UAE. The collected data were analyzed using NVIVO software and grounded theory was used for thematic analysis. Results This study identified factors specific to the deployment of AI to transform patient care in the UAE. First, physicians must control the applications and be fully trained and engaged in the testing phase. Second, healthcare systems need to be connected, and the AI outcomes need to be easily interpretable by physicians. Third, the reimbursement for AI-based applications should be settled by insurance or the government. Fourth, patients should be aware of and accept the technology before physicians use it to avoid negative consequences for the doctor-patient relationship. Conclusions This research was conducted with practicing physicians in the UAE to determine their understanding of enabling and derailing factors for improving patient care through AI-based applications. The importance of involving physicians as the accountable agents for AI tools is highlighted. Public awareness regarding AI in healthcare should be improved to drive public acceptance.
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Affiliation(s)
| | - Markus Bick
- ESCP Business School, Information & Operations Management, Berlin, Germany
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32
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Fazakarley CA, Breen M, Leeson P, Thompson B, Williamson V. Experiences of using artificial intelligence in healthcare: a qualitative study of UK clinician and key stakeholder perspectives. BMJ Open 2023; 13:e076950. [PMID: 38081671 PMCID: PMC10729128 DOI: 10.1136/bmjopen-2023-076950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Accepted: 11/24/2023] [Indexed: 12/18/2023] Open
Abstract
OBJECTIVES Artificial intelligence (AI) is a rapidly developing field in healthcare, with tools being developed across various specialties to support healthcare professionals and reduce workloads. It is important to understand the experiences of professionals working in healthcare to ensure that future AI tools are acceptable and effectively implemented. The aim of this study was to gain an in-depth understanding of the experiences and perceptions of UK healthcare workers and other key stakeholders about the use of AI in the National Health Service (NHS). DESIGN A qualitative study using semistructured interviews conducted remotely via MS Teams. Thematic analysis was carried out. SETTING NHS and UK higher education institutes. PARTICIPANTS Thirteen participants were recruited, including clinical and non-clinical participants working for the NHS and researchers working to develop AI tools for healthcare settings. RESULTS Four core themes were identified: positive perceptions of AI; potential barriers to using AI in healthcare; concerns regarding AI use and steps needed to ensure the acceptability of future AI tools. Overall, we found that those working in healthcare were generally open to the use of AI and expected it to have many benefits for patients and facilitate access to care. However, concerns were raised regarding the security of patient data, the potential for misdiagnosis and that AI could increase the burden on already strained healthcare staff. CONCLUSION This study found that healthcare staff are willing to engage with AI research and incorporate AI tools into care pathways. Going forward, the NHS and AI developers will need to collaborate closely to ensure that future tools are suitable for their intended use and do not negatively impact workloads or patient trust. Future AI studies should continue to incorporate the views of key stakeholders to improve tool acceptability. TRIAL REGISTRATION NUMBER NCT05028179; ISRCTN15113915; IRAS ref: 293515.
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Affiliation(s)
| | - Maria Breen
- School of Psychology & Clinical Language Sciences, University of Reading, Reading, UK
- Breen Clinical Research, London, UK
| | - Paul Leeson
- Division of Cardiovascular Medicine, University of Oxford, Oxford, UK
| | | | - Victoria Williamson
- King's College London, London, UK
- Experimental Psychology, University of Oxford, Oxford, UK
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Khavandi S, Zaghloul F, Higham A, Lim E, de Pennington N, Celi LA. Investigating the Impact of Automation on the Health Care Workforce Through Autonomous Telemedicine in the Cataract Pathway: Protocol for a Multicenter Study. JMIR Res Protoc 2023; 12:e49374. [PMID: 38051569 PMCID: PMC10731565 DOI: 10.2196/49374] [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: 05/26/2023] [Revised: 09/17/2023] [Accepted: 09/18/2023] [Indexed: 12/07/2023] Open
Abstract
BACKGROUND While digital health innovations are increasingly being adopted by health care organizations, implementation is often carried out without considering the impacts on frontline staff who will be using the technology and who will be affected by its introduction. The enthusiasm surrounding the use of artificial intelligence (AI)-enabled digital solutions in health care is tempered by uncertainty around how it will change the working lives and practices of health care professionals. Digital enablement can be viewed as facilitating enhanced effectiveness and efficiency by improving services and automating cognitive labor, yet the implementation of such AI technology comes with challenges related to changes in work practices brought by automation. This research explores staff experiences before and after care pathway automation with an autonomous clinical conversational assistant, Dora (Ufonia Ltd), that is able to automate routine clinical conversations. OBJECTIVE The primary objective is to examine the impact of AI-enabled automation on clinicians, allied health professionals, and administrators who provide or facilitate health care to patients in high-volume, low-complexity care pathways. In the process of transforming care pathways through automation of routine tasks, staff will increasingly "work at the top of their license." The impact of this fundamental change on the professional identity, well-being, and work practices of the individual is poorly understood at present. METHODS We will adopt a multiple case study approach, combining qualitative and quantitative data collection methods, over 2 distinct phases, namely phase A (preimplementation) and phase B (postimplementation). RESULTS The analysis is expected to reveal the interrelationship between Dora and those affected by its introduction. This will reveal how tasks and responsibilities have changed or shifted, current tensions and contradictions, ways of working, and challenges, benefits, and opportunities as perceived by those on the frontlines of the health care system. The findings will enable a better understanding of the resistance or susceptibility of different stakeholders within the health care workforce and encourage managerial awareness of differing needs, demands, and uncertainties. CONCLUSIONS The implementation of AI in the health care sector, as well as the body of research on this topic, remain in their infancy. The project's key contribution will be to understand the impact of AI-enabled automation on the health care workforce and their work practices. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) PRR1-10.2196/49374.
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Affiliation(s)
- Sarah Khavandi
- Ufonia, Oxford, United Kingdom
- Gloucestershire Hospitals NHS Foundation Trust, Cheltenham, United Kingdom
- Imperial College School of Medicine, Imperial College London, London, United Kingdom
| | - Fatema Zaghloul
- Operations and Management Science, Healthcare and Innovation, University of Bristol, Bristol, United Kingdom
| | - Aisling Higham
- Ufonia, Oxford, United Kingdom
- Royal Berkshire NHS Foundation Trust, Reading, United Kingdom
| | - Ernest Lim
- Ufonia, Oxford, United Kingdom
- Department of Computer Science, University of York, York, United Kingdom
| | | | - Leo Anthony Celi
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA, United States
- Division of Pulmonary, Critical Care and Sleep Medicine, Beth Israel Deaconess Medical Center, Boston, MA, United States
- Department of Biostatistics, Harvard TH Chan School of Public Health, Boston, MA, United States
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Zainal H, Xiaohui X, Thumboo J, Kok Yong F. Digital competencies for Singapore's national medical school curriculum: a qualitative study. MEDICAL EDUCATION ONLINE 2023; 28:2211820. [PMID: 37186901 PMCID: PMC10187105 DOI: 10.1080/10872981.2023.2211820] [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: 02/01/2023] [Accepted: 05/04/2023] [Indexed: 05/17/2023]
Abstract
Studies have shown that national-level initiatives to equip medical students with relevant digital competencies carry many benefits. Yet, few countries have outlined such competencies for clinical practice in the core medical school curriculum. This paper identifies current training gaps at the national level in digital competencies needed by students in the formal curricula of all three medical schools in Singapore from the perspectives of clinical educators and institutional leaders. It bears implications for countries that intend to implement standardized learning objectives for training in these digital competencies. Findings were drawn from in-depth interviews with 19 clinical educators and leaders of local medical schools. Participants were recruited using purposive sampling. Data were interpreted using qualitative thematic analysis. Thirteen of the participants were clinical educators while 6 were deans or vice deans of education from one of the three medical schools in Singapore. While the schools have introduced some relevant courses, they are not standardized nationally. Moreover, the school's niche areas have not been leveraged upon for training in digital competencies. Participants across all schools acknowledged that more formal training is needed in digital health, data management, and applying the principles of digital technologies. Participants also noted that the healthcare needs of the population, patient safety, and safe procedures in the utilisation of digital healthcare technologies should be prioritized when determining the competencies needed by students. Additionally, participants highlighted the need for stronger collaboration among medical schools, and for a stronger link between current curriculum and clinical practice. The findings highlighted the need for better collaboration among medical schools in the sharing of educational resources and expertise. Furthermore, stronger collaborations with professional bodies and the healthcare system should be established to ensure that the goals and outcomes of medical education and the healthcare system are aligned.
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Affiliation(s)
- Humairah Zainal
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
| | - Xin Xiaohui
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
| | - Julian Thumboo
- Health Services Research Unit, Singapore General Hospital, Singapore, Singapore
- Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
| | - Fong Kok Yong
- Department of Rheumatology and Immunology, Singapore General Hospital, Singapore, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore, Singapore
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Nilsen P, Svedberg P, Neher M, Nair M, Larsson I, Petersson L, Nygren J. A Framework to Guide Implementation of AI in Health Care: Protocol for a Cocreation Research Project. JMIR Res Protoc 2023; 12:e50216. [PMID: 37938896 PMCID: PMC10666006 DOI: 10.2196/50216] [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: 06/23/2023] [Revised: 08/16/2023] [Accepted: 09/08/2023] [Indexed: 11/10/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) has the potential in health care to transform patient care and administrative processes, yet health care has been slow to adopt AI due to many types of barriers. Implementation science has shown the importance of structured implementation processes to overcome implementation barriers. However, there is a lack of knowledge and tools to guide such processes when implementing AI-based applications in health care. OBJECTIVE The aim of this protocol is to describe the development, testing, and evaluation of a framework, "Artificial Intelligence-Quality Implementation Framework" (AI-QIF), intended to guide decisions and activities related to the implementation of various AI-based applications in health care. METHODS The paper outlines the development of an AI implementation framework for broad use in health care based on the Quality Implementation Framework (QIF). QIF is a process model developed in implementation science. The model guides the user to consider implementation-related issues in a step-by-step design and plan and perform activities that support implementation. This framework was chosen for its adaptability, usability, broad scope, and detailed guidance concerning important activities and considerations for successful implementation. The development will proceed in 5 phases with primarily qualitative methods being used. The process starts with phase I, in which an AI-adapted version of QIF is created (AI-QIF). Phase II will produce a digital mockup of the AI-QIF. Phase III will involve the development of a prototype of the AI-QIF with an intuitive user interface. Phase IV is dedicated to usability testing of the prototype in health care environments. Phase V will focus on evaluating the usability and effectiveness of the AI-QIF. Cocreation is a guiding principle for the project and is an important aspect in 4 of the 5 development phases. The cocreation process will enable the use of both on research-based and practice-based knowledge. RESULTS The project is being conducted within the frame of a larger research program, with the overall objective of developing theoretically and empirically informed frameworks to support AI implementation in routine health care. The program was launched in 2021 and has carried out numerous research activities. The development of AI-QIF as a tool to guide the implementation of AI-based applications in health care will draw on knowledge and experience acquired from these activities. The framework is being developed over 2 years, from January 2023 to December 2024. It is under continuous development and refinement. CONCLUSIONS The development of the AI implementation framework, AI-QIF, described in this study protocol aims to facilitate the implementation of AI-based applications in health care based on the premise that implementation processes benefit from being well-prepared and structured. The framework will be coproduced to enhance its relevance, validity, usefulness, and potential value for application in practice. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/50216.
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Affiliation(s)
- Per Nilsen
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Margit Neher
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Monika Nair
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Ingrid Larsson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Lena Petersson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Sant'Anna A, Nygren J. A Pragmatic Mapping of Perceptions and Use of Digital Information Systems in Primary Care in Sweden: Survey Study. Interact J Med Res 2023; 12:e49973. [PMID: 37878357 PMCID: PMC10632913 DOI: 10.2196/49973] [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: 06/15/2023] [Revised: 09/01/2023] [Accepted: 09/27/2023] [Indexed: 10/26/2023] Open
Abstract
BACKGROUND Electronic health records and IT infrastructure in primary care allow for digital documentation and access to information, which can be used to guide evidence-based care and monitor patient safety and quality of care. Quality indicators specified by regulatory authorities can be automatically computed and presented to primary care staff. However, the implementation of digital information systems (DIS) in health care can be challenging, and understanding factors such as relative advantage, compatibility, complexity, trialability, and observability is needed to improve the success and rate of adoption and diffusion. OBJECTIVE This study aims to explore how DIS are used and perceived by health care professionals in primary care. METHODS This study used quantitative assessment to gather survey data on the use and potential of DIS in health care in Sweden from the perspectives of primary care personnel in various roles. The digital questionnaire was designed to be short and contained 3 sections covering respondent characteristics, current use of platforms, and perceptions of decision support tools. Data were analyzed using descriptive statistics, nonparametric hypothesis testing, ordinal coefficient α, and confirmatory factor analysis. RESULTS The study collected responses from participants across 10 regions of Sweden, comprising 31.9% (n=22) from private clinics and 68.1% (n=47) from public clinics. Participants included administrators (18/69, 26.1%), a medical strategist (1/69, 1.4%), and physicians (50/69, 72.5%). Usage frequency varied as follows: 11.6% (n=8) used DIS weekly, 24.6% (n=17) monthly, 27.5% (n=19) a few times a year, 26.1% (n=18) very rarely, and 10.1% (n=7) lacked access. Administrators used DIS more frequently than physicians (P=.005). DIS use centered on quality improvement and identifying high-risk patients, with differences by role. Physicians were more inclined to use DIS out of curiosity (P=.01). Participants desired DIS for patient follow-up, lifestyle guidance, treatment suggestions, reminders, and shared decision-making. Administrators favored predictive analysis (P<.001), while physicians resisted immediate patient identification (P=.03). The 5 innovation attributes showed high internal consistency (α>.7). These factors explained 78.5% of questionnaire variance, relating to complexity, competitive advantage, compatibility, trialability, and observability. Factors 2, 3, and 4 predicted intention to use DIS, with factor 2 alone achieving the best accuracy (root-mean-square=0.513). CONCLUSIONS Administrators and physicians exhibited role-based DIS use patterns highlighting the need for tailored approaches to promote DIS adoption. The study reveals a link between positive perceptions and intention to use DIS, emphasizing the significance of considering all factors for successful health care integration. The results suggest various directions for future studies. These include refining the trialability and observability questions for increased reliability and validity, investigating a larger sample with more specific target groups to improve generalization, and exploring the relevance of different groups' perspectives and needs in relation to decisions about and use of DIS.
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Affiliation(s)
| | - Jens Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Ardito V, Cappellaro G, Compagni A, Petracca F, Preti LM. Implementation of Machine Learning Applications in Health Care Organizations: Protocol for a Systematic Review of Empirical Studies. JMIR Res Protoc 2023; 12:e47971. [PMID: 37698910 PMCID: PMC10523208 DOI: 10.2196/47971] [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: 04/06/2023] [Revised: 06/11/2023] [Accepted: 07/17/2023] [Indexed: 09/13/2023] Open
Abstract
BACKGROUND An increasing interest in machine learning (ML) has been observed among scholars and health care professionals. However, while ML-based applications have been shown to be effective and have the potential to change the delivery of patient care, their implementation in health care organizations is complex. There are several challenges that currently hamper the uptake of ML in daily practice, and there is currently limited knowledge on how these challenges have been addressed in empirical studies on implemented ML-based applications. OBJECTIVE The aim of this systematic literature review is twofold: (1) to map the ML-based applications implemented in health care organizations, with a focus on investigating the organizational dimensions that are relevant in the implementation process; and (2) to analyze the processes and strategies adopted to foster a successful uptake of ML. METHODS We developed this protocol following the PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) guidelines. The search was conducted on 3 databases (PubMed, Scopus, and Web of Science), considering a 10-year time frame (2013-2023). The search strategy was built around 4 blocks of keywords (artificial intelligence, implementation, health care, and study type). Based on the detailed inclusion criteria defined, only empirical studies documenting the implementation of ML-based applications used by health care professionals in clinical settings will be considered. The study protocol was registered in PROSPERO (International Prospective Register of Systematic Reviews). RESULTS The review is ongoing and is expected to be completed by September 2023. Data analysis is currently underway, and the first results are expected to be submitted for publication in November 2023. The study was funded by the European Union within the Multilayered Urban Sustainability Action (MUSA) project. CONCLUSIONS ML-based applications involving clinical decision support and automation of clinical tasks present unique traits that add several layers of complexity compared with earlier health technologies. Our review aims at contributing to the existing literature by investigating the implementation of ML from an organizational perspective and by systematizing a conspicuous amount of information on factors influencing implementation. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/47971.
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Affiliation(s)
- Vittoria Ardito
- Center for Research on Health and Social Care Management (CERGAS), SDA Bocconi School of Management, Milan, Italy
| | - Giulia Cappellaro
- Center for Research on Health and Social Care Management (CERGAS), SDA Bocconi School of Management, Milan, Italy
- Department of Social and Political Sciences, Bocconi University, Milan, Italy
| | - Amelia Compagni
- Center for Research on Health and Social Care Management (CERGAS), SDA Bocconi School of Management, Milan, Italy
- Department of Social and Political Sciences, Bocconi University, Milan, Italy
| | - Francesco Petracca
- Center for Research on Health and Social Care Management (CERGAS), SDA Bocconi School of Management, Milan, Italy
| | - Luigi Maria Preti
- Center for Research on Health and Social Care Management (CERGAS), SDA Bocconi School of Management, Milan, Italy
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Steerling E, Siira E, Nilsen P, Svedberg P, Nygren J. Implementing AI in healthcare-the relevance of trust: a scoping review. FRONTIERS IN HEALTH SERVICES 2023; 3:1211150. [PMID: 37693234 PMCID: PMC10484529 DOI: 10.3389/frhs.2023.1211150] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 08/11/2023] [Indexed: 09/12/2023]
Abstract
Background The process of translation of AI and its potential benefits into practice in healthcare services has been slow in spite of its rapid development. Trust in AI in relation to implementation processes is an important aspect. Without a clear understanding, the development of effective implementation strategies will not be possible, nor will AI advance despite the significant investments and possibilities. Objective This study aimed to explore the scientific literature regarding how trust in AI in relation to implementation in healthcare is conceptualized and what influences trust in AI in relation to implementation in healthcare. Methods This scoping review included five scientific databases. These were searched to identify publications related to the study aims. Articles were included if they were published in English, after 2012, and peer-reviewed. Two independent reviewers conducted an abstract and full-text review, as well as carrying out a thematic analysis with an inductive approach to address the study aims. The review was reported in accordance with the PRISMA-ScR guidelines. Results A total of eight studies were included in the final review. We found that trust was conceptualized in different ways. Most empirical studies had an individual perspective where trust was directed toward the technology's capability. Two studies focused on trust as relational between people in the context of the AI application rather than as having trust in the technology itself. Trust was also understood by its determinants and as having a mediating role, positioned between characteristics and AI use. The thematic analysis yielded three themes: individual characteristics, AI characteristics and contextual characteristics, which influence trust in AI in relation to implementation in healthcare. Conclusions Findings showed that the conceptualization of trust in AI differed between the studies, as well as which determinants they accounted for as influencing trust. Few studies looked beyond individual characteristics and AI characteristics. Future empirical research addressing trust in AI in relation to implementation in healthcare should have a more holistic view of the concept to be able to manage the many challenges, uncertainties, and perceived risks.
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Affiliation(s)
- Emilie Steerling
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Elin Siira
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Per Nilsen
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
- Department of Health, Medicine and Caring Sciences, Linköping University, Linköping, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Nair M, Andersson J, Nygren JM, Lundgren LE. Barriers and Enablers for Implementation of an Artificial Intelligence-Based Decision Support Tool to Reduce the Risk of Readmission of Patients With Heart Failure: Stakeholder Interviews. JMIR Form Res 2023; 7:e47335. [PMID: 37610799 PMCID: PMC10483295 DOI: 10.2196/47335] [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: 03/16/2023] [Revised: 05/29/2023] [Accepted: 05/31/2023] [Indexed: 08/24/2023] Open
Abstract
BACKGROUND Artificial intelligence (AI) applications in health care are expected to provide value for health care organizations, professionals, and patients. However, the implementation of such systems should be carefully planned and organized in order to ensure quality, safety, and acceptance. The gathered view of different stakeholders is a great source of information to understand the barriers and enablers for implementation in a specific context. OBJECTIVE This study aimed to understand the context and stakeholder perspectives related to the future implementation of a clinical decision support system for predicting readmissions of patients with heart failure. The study was part of a larger project involving model development, interface design, and implementation planning of the system. METHODS Interviews were held with 12 stakeholders from the regional and municipal health care organizations to gather their views on the potential effects implementation of such a decision support system could have as well as barriers and enablers for implementation. Data were analyzed based on the categories defined in the nonadoption, abandonment, scale-up, spread, sustainability (NASSS) framework. RESULTS Stakeholders had in general a positive attitude and curiosity toward AI-based decision support systems, and mentioned several barriers and enablers based on the experiences of previous implementations of information technology systems. Central aspects to consider for the proposed clinical decision support system were design aspects, access to information throughout the care process, and integration into the clinical workflow. The implementation of such a system could lead to a number of effects related to both clinical outcomes as well as resource allocation, which are all important to address in the planning of implementation. Stakeholders saw, however, value in several aspects of implementing such system, emphasizing the increased quality of life for those patients who can avoid being hospitalized. CONCLUSIONS Several ideas were put forward on how the proposed AI system would potentially affect and provide value for patients, professionals, and the organization, and implementation aspects were important parts of that. A successful system can help clinicians to prioritize the need for different types of treatments but also be used for planning purposes within the hospital. However, the system needs not only technological and clinical precision but also a carefully planned implementation process. Such a process should take into consideration the aspects related to all the categories in the NASSS framework. This study further highlighted the importance to study stakeholder needs early in the process of development, design, and implementation of decision support systems, as the data revealed new information on the potential use of the system and the placement of the application in the care process.
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Affiliation(s)
- Monika Nair
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | | | - Jens M Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Lina E Lundgren
- School of Business, Innovation and Sustainability, Halmstad University, Halmstad, Sweden
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Zammarchi I, Santacroce G, Iacucci M. Next-Generation Endoscopy in Inflammatory Bowel Disease. Diagnostics (Basel) 2023; 13:2547. [PMID: 37568910 PMCID: PMC10417286 DOI: 10.3390/diagnostics13152547] [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: 07/10/2023] [Revised: 07/28/2023] [Accepted: 07/29/2023] [Indexed: 08/13/2023] Open
Abstract
Endoscopic healing is recognized as a primary treatment goal in Inflammatory Bowel Disease (IBD). However, endoscopic remission may not reflect histological remission, which is crucial to achieving favorable long-term outcomes. The development of new advanced techniques has revolutionized the field of IBD assessment and management. These tools can accurately assess vascular and mucosal features, drawing endoscopy closer to histology. Moreover, they can enhance the detection and characterization of IBD-related dysplasia. Given the persistent challenge of interobserver variability, a more standardized approach to endoscopy is warranted, and the integration of artificial intelligence (AI) holds promise for addressing this limitation. Additionally, although molecular endoscopy is still in its infancy, it is a promising tool to forecast response to therapy. This review provides an overview of advanced endoscopic techniques, including dye-based and dye-less chromoendoscopy, and in vivo histological examinations with probe-based confocal laser endomicroscopy and endocytoscopy. The remarkable contribution of these tools to IBD management, especially when integrated with AI, is discussed. Specific attention is given to their role in improving disease assessment, detection, and characterization of IBD-associated lesions, and predicting disease-related outcomes.
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Affiliation(s)
| | | | - Marietta Iacucci
- APC Microbiome Ireland, College of Medicine and Health, University College Cork, T12 R229 Cork, Ireland; (I.Z.); (G.S.)
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Anil S, Porwal P, Porwal A. Transforming Dental Caries Diagnosis Through Artificial Intelligence-Based Techniques. Cureus 2023; 15:e41694. [PMID: 37575741 PMCID: PMC10413921 DOI: 10.7759/cureus.41694] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/11/2023] [Indexed: 08/15/2023] Open
Abstract
Diagnosing dental caries plays a pivotal role in preventing and treating tooth decay. However, traditional methods of diagnosing caries often fall short in accuracy and efficiency. Despite the endorsement of radiography as a diagnostic tool, the identification of dental caries through radiographic images can be influenced by individual interpretation. Incorporating artificial intelligence (AI) into diagnosing dental caries holds significant promise, potentially enhancing the precision and efficiency of diagnoses. This review introduces the fundamental concepts of AI, including machine learning and deep learning algorithms, and emphasizes their relevance and potential contributions to the diagnosis of dental caries. It further explains the process of gathering and pre-processing radiography data for AI examination. Additionally, AI techniques for dental caries diagnosis are explored, focusing on image processing, analysis, and classification models for predicting caries risk and severity. Deep learning applications in dental caries diagnosis using convolutional neural networks are presented. Furthermore, the integration of AI systems into dental practice is discussed, including the challenges and considerations for implementation as well as ethical and legal aspects. The breadth of AI technologies and their prospective utility in clinical scenarios for diagnosing dental caries from dental radiographs is presented. This review outlines the advancements of AI and its potential in revolutionizing dental caries diagnosis, encouraging further research and development in this rapidly evolving field.
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Affiliation(s)
| | - Priyanka Porwal
- Dentistry, Pushpagiri Institute of Medical Sciences and Research Centre, Tiruvalla, IND
| | - Amit Porwal
- Prosthetic Dental Sciences, College of Dentistry, Jazan University, Jazan, SAU
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Imrie F, Cebere B, McKinney EF, van der Schaar M. AutoPrognosis 2.0: Democratizing diagnostic and prognostic modeling in healthcare with automated machine learning. PLOS DIGITAL HEALTH 2023; 2:e0000276. [PMID: 37347752 PMCID: PMC10287005 DOI: 10.1371/journal.pdig.0000276] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Accepted: 05/17/2023] [Indexed: 06/24/2023]
Abstract
Diagnostic and prognostic models are increasingly important in medicine and inform many clinical decisions. Recently, machine learning approaches have shown improvement over conventional modeling techniques by better capturing complex interactions between patient covariates in a data-driven manner. However, the use of machine learning introduces technical and practical challenges that have thus far restricted widespread adoption of such techniques in clinical settings. To address these challenges and empower healthcare professionals, we present an open-source machine learning framework, AutoPrognosis 2.0, to facilitate the development of diagnostic and prognostic models. AutoPrognosis leverages state-of-the-art advances in automated machine learning to develop optimized machine learning pipelines, incorporates model explainability tools, and enables deployment of clinical demonstrators, without requiring significant technical expertise. To demonstrate AutoPrognosis 2.0, we provide an illustrative application where we construct a prognostic risk score for diabetes using the UK Biobank, a prospective study of 502,467 individuals. The models produced by our automated framework achieve greater discrimination for diabetes than expert clinical risk scores. We have implemented our risk score as a web-based decision support tool, which can be publicly accessed by patients and clinicians. By open-sourcing our framework as a tool for the community, we aim to provide clinicians and other medical practitioners with an accessible resource to develop new risk scores, personalized diagnostics, and prognostics using machine learning techniques. Software: https://github.com/vanderschaarlab/AutoPrognosis.
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Affiliation(s)
- Fergus Imrie
- Department of Electrical and Computer Engineering, University of California, Los Angeles, California, United States of America
| | - Bogdan Cebere
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
| | - Eoin F. McKinney
- Department of Medicine, University of Cambridge, Cambridge, United Kingdom
| | - Mihaela van der Schaar
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, United Kingdom
- The Alan Turing Institute, London, United Kingdom
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Al Meslamani AZ. Beyond implementation: the long-term economic impact of AI in healthcare. J Med Econ 2023; 26:1566-1569. [PMID: 37975706 DOI: 10.1080/13696998.2023.2285186] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 11/15/2023] [Indexed: 11/19/2023]
Affiliation(s)
- Ahmad Z Al Meslamani
- College of Pharmacy, Al Ain University, Abu Dhabi, UAE
- AAU Health and Biomedical Research Center, Al Ain University, Abu Dhabi, UAE
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Čartolovni A, Malešević A, Poslon L. Critical analysis of the AI impact on the patient-physician relationship: A multi-stakeholder qualitative study. Digit Health 2023; 9:20552076231220833. [PMID: 38130798 PMCID: PMC10734361 DOI: 10.1177/20552076231220833] [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: 08/04/2023] [Accepted: 11/29/2023] [Indexed: 12/23/2023] Open
Abstract
Objective This qualitative study aims to present the aspirations, expectations and critical analysis of the potential for artificial intelligence (AI) to transform patient-physician relationship, according to multi-stakeholder insight. Methods This study was conducted from June to December 2021, using an anticipatory ethics approach and sociology of expectations as the theoretical frameworks. It focused mainly on three groups of stakeholders; namely, physicians (n = 12), patients (n = 15) and healthcare managers (n = 11), all of whom are directly related to the adoption of AI in medicine (n = 38). Results In this study, interviews were conducted with 40% of the patients in the sample (15/38), as well as 31% of the physicians (12/38) and 29% of health managers in the sample (11/38). The findings highlight the following: (1) the impact of AI on fundamental aspects of the patient-physician relationship and the underlying importance of a synergistic relationship between the physician and AI; (2) the potential for AI to alleviate workload and reduce administrative burden by saving time and putting the patient at the centre of the caring process and (3) the potential risk to the holistic approach by neglecting humanness in healthcare. Conclusions This multi-stakeholder qualitative study, which focused on the micro-level of healthcare decision-making, sheds new light on the impact of AI on healthcare and the potential transformation of patient-physician relationship. The results of the current study highlight the need to adopt a critical awareness approach to the implementation of AI in healthcare by applying critical thinking and reasoning. It is important not to rely solely upon the recommendations of AI while neglecting clinical reasoning and physicians' knowledge of best clinical practices. Instead, it is vital that the core values of the existing patient-physician relationship - such as trust and honesty, conveyed through open and sincere communication - are preserved.
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Affiliation(s)
- Anto Čartolovni
- Digital Healthcare Ethics Laboratory (Digit-HeaL), Catholic University of Croatia, Zagreb, Croatia
- School of Medicine, Catholic University of Croatia, Zagreb, Croatia
| | - Anamaria Malešević
- Digital Healthcare Ethics Laboratory (Digit-HeaL), Catholic University of Croatia, Zagreb, Croatia
| | - Luka Poslon
- Digital Healthcare Ethics Laboratory (Digit-HeaL), Catholic University of Croatia, Zagreb, Croatia
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Petersson L, Vincent K, Svedberg P, Nygren JM, Larsson I. Ethical considerations in implementing AI for mortality prediction in the emergency department: Linking theory and practice. Digit Health 2023; 9:20552076231206588. [PMID: 37829612 PMCID: PMC10566278 DOI: 10.1177/20552076231206588] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/21/2023] [Indexed: 10/14/2023] Open
Abstract
Background Artificial intelligence (AI) is predicted to be a solution for improving healthcare, increasing efficiency, and saving time and recourses. A lack of ethical principles for the use of AI in practice has been highlighted by several stakeholders due to the recent attention given to it. Research has shown an urgent need for more knowledge regarding the ethical implications of AI applications in healthcare. However, fundamental ethical principles may not be sufficient to describe ethical concerns associated with implementing AI applications. Objective The aim of this study is twofold, (1) to use the implementation of AI applications to predict patient mortality in emergency departments as a setting to explore healthcare professionals' perspectives on ethical issues in relation to ethical principles and (2) to develop a model to guide ethical considerations in AI implementation in healthcare based on ethical theory. Methods Semi-structured interviews were conducted with 18 participants. The abductive approach used to analyze the empirical data consisted of four steps alternating between inductive and deductive analyses. Results Our findings provide an ethical model demonstrating the need to address six ethical principles (autonomy, beneficence, non-maleficence, justice, explicability, and professional governance) in relation to ethical theories defined as virtue, deontology, and consequentialism when AI applications are to be implemented in clinical practice. Conclusions Ethical aspects of AI applications are broader than the prima facie principles of medical ethics and the principle of explicability. Ethical aspects thus need to be viewed from a broader perspective to cover different situations that healthcare professionals, in general, and physicians, in particular, may face when using AI applications in clinical practice.
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Affiliation(s)
- Lena Petersson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Kalista Vincent
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens M Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Ingrid Larsson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Wiesenfeld BM, Aphinyanaphongs Y, Nov O. AI model transferability in healthcare: a sociotechnical perspective. NAT MACH INTELL 2022. [DOI: 10.1038/s42256-022-00544-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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El Emam K, Malin B. Introducing JMIR AI. JMIR AI 2022; 1:e42046. [PMID: 38875542 PMCID: PMC11041454 DOI: 10.2196/42046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/19/2022] [Accepted: 08/22/2022] [Indexed: 06/16/2024]
Abstract
JMIR AI is a new journal with a focus on publishing applied artificial intelligence and machine learning research. This editorial provides an overview of the primary objectives, the focus areas of the journal, and the types of articles that are within scope.
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Affiliation(s)
- Khaled El Emam
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON, Canada
| | - Bradley Malin
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, United States
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