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Moretti V, Pronzato R. The emotional ambiguities of healthcare professionals' platform experiences. Soc Sci Med 2024; 357:117185. [PMID: 39142145 DOI: 10.1016/j.socscimed.2024.117185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 07/30/2024] [Accepted: 08/03/2024] [Indexed: 08/16/2024]
Abstract
This paper investigates how healthcare professionals experience digital platforms in their work practices and how these relationships enable forms of emotional labour and contribute to shaping their emotional health. Methodologically, the contribution draws on audio-diaries kept by 15 healthcare professionals and a final semi-structured interview conducted with the same informants. The research material was analysed using open and axial coding techniques, in a grounded theory fashion. Findings provides meaningful insights to the literature on the emotional labour of healthcare professionals, as well as to studies on digital health and labour. Specifically, we show that participants associate different and even contrasting reflections and emotional states with their relationships with digital platforms. Thus, there is not exclusively one trajectory that can explain the implications of media uses, as different and potentially conflicting emotions coexist within the same experience. Given this scenario, we argue that it can be fruitful to use the lens of 'ambiguity' to scrutinise the ambivalences and tensions characterising platform experiences, and how emotional labour in healthcare intertwines with technological developments. Moreover, we advocate for the development of critical digital literacy skills among healthcare professionals.
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Affiliation(s)
- Veronica Moretti
- University of Bologna, Department of Sociology and Business Law, Strada Maggiore 45, Bologna, Italy.
| | - Riccardo Pronzato
- University of Bologna, Department of Sociology and Business Law, Strada Maggiore 45, Bologna, Italy.
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2
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Vilela E, Fontes-Carvalho R. "Science and Charity": Picasso's Lessons for Medical Practice. JACC Case Rep 2024; 29:102353. [PMID: 38827266 PMCID: PMC11143901 DOI: 10.1016/j.jaccas.2024.102353] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2024]
Affiliation(s)
- Eduardo Vilela
- Cardiology Department, Unidade Local de Saúde de Gaia e Espinho, Vila Nova de Gaia, Portugal
| | - Ricardo Fontes-Carvalho
- Cardiology Department, Unidade Local de Saúde de Gaia e Espinho, Vila Nova de Gaia, Portugal
- UnIC@RISE, Department of Surgery and Physiology, Faculty of Medicine of the University of Porto, Porto, Portugal
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Graham Y, Spencer AE, Velez GE, Herbell K. Engaging Youth Voice and Family Partnerships to Improve Children's Mental Health Outcomes. Child Adolesc Psychiatr Clin N Am 2024; 33:343-354. [PMID: 38823808 DOI: 10.1016/j.chc.2024.02.004] [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] [Indexed: 06/03/2024]
Abstract
Promoting active participation of families and youth in mental health systems of care is the cornerstone of creating a more inclusive, effective, and responsive care network. This article focuses on the inclusion of parent and youth voice in transforming our mental health care system to promote increased engagement at all levels of service delivery. Youth and parent peer support delivery models, digital innovation, and technology not only empower the individuals involved, but also have the potential to enhance the overall efficacy of the mental health care system.
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Affiliation(s)
- Yolanda Graham
- Morehouse School of Medicine, Devereux Advanced Behavioral Health, 444 Devereux Drive, Villanova, PA 19085, USA.
| | - Andrea E Spencer
- Ann & Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, 225 East Chicago Avenue, Chicago, IL 60611, USA
| | - German E Velez
- New York-Presbyterian Hospital, Weill Cornell Medical College/ Columbia University College of Physicians and Surgeons, 525 E. 68th Street, Box 140, New York, NY 10065, USA
| | - Kayla Herbell
- Martha S. Pitzer Center for Women, Children, and Youth, The Ohio State University, 1577 Neil Avenue, Columbus, OH 43210, USA
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Jo MH, Kim MJ, Oh HK, Choi MJ, Shin HR, Lee TG, Ahn HM, Kim DW, Kang SB. Communicative competence of generative artificial intelligence in responding to patient queries about colorectal cancer surgery. Int J Colorectal Dis 2024; 39:94. [PMID: 38902500 PMCID: PMC11189990 DOI: 10.1007/s00384-024-04670-3] [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] [Accepted: 06/13/2024] [Indexed: 06/22/2024]
Abstract
PURPOSE To examine the ability of generative artificial intelligence (GAI) to answer patients' questions regarding colorectal cancer (CRC). METHODS Ten clinically relevant questions about CRC were selected from top-rated hospitals' websites and patient surveys and presented to three GAI tools (Chatbot Generative Pre-Trained Transformer [GPT-4], Google Bard, and CLOVA X). Their responses were compared with answers from the CRC information book. Response evaluation was performed by two groups, each consisting of five healthcare professionals (HCP) and patients. Each question was scored on a 1-5 Likert scale based on four evaluation criteria (maximum score, 20 points/question). RESULTS In an analysis including only HCPs, the information book scored 11.8 ± 1.2, GPT-4 scored 13.5 ± 1.1, Google Bard scored 11.5 ± 0.7, and CLOVA X scored 12.2 ± 1.4 (P = 0.001). The score of GPT-4 was significantly higher than those of the information book (P = 0.020) and Google Bard (P = 0.001). In an analysis including only patients, the information book scored 14.1 ± 1.4, GPT-4 scored 15.2 ± 1.8, Google Bard scored 15.5 ± 1.8, and CLOVA X scored 14.4 ± 1.8, without significant differences (P = 0.234). When both groups of evaluators were included, the information book scored 13.0 ± 0.9, GPT-4 scored 14.4 ± 1.2, Google Bard scored 13.5 ± 1.0, and CLOVA X scored 13.3 ± 1.5 (P = 0.070). CONCLUSION The three GAIs demonstrated similar or better communicative competence than the information book regarding questions related to CRC surgery in Korean. If high-quality medical information provided by GAI is supervised properly by HCPs and published as an information book, it could be helpful for patients to obtain accurate information and make informed decisions.
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Affiliation(s)
- Min Hyeong Jo
- Department of Surgery, Seoul National University Bundang Hospital, 300 Gumi-dong Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
| | - Min-Jun Kim
- Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea
| | - Heung-Kwon Oh
- Department of Surgery, Seoul National University Bundang Hospital, 300 Gumi-dong Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea.
- Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea.
| | - Mi Jeong Choi
- Department of Surgery, Seoul National University Bundang Hospital, 300 Gumi-dong Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
| | - Hye-Rim Shin
- Department of Surgery, Seoul National University Bundang Hospital, 300 Gumi-dong Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
| | - Tae-Gyun Lee
- Department of Surgery, Seoul National University Bundang Hospital, 300 Gumi-dong Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
| | - Hong-Min Ahn
- Department of Surgery, Seoul National University Bundang Hospital, 300 Gumi-dong Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
| | - Duck-Woo Kim
- Department of Surgery, Seoul National University Bundang Hospital, 300 Gumi-dong Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
- Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea
| | - Sung-Bum Kang
- Department of Surgery, Seoul National University Bundang Hospital, 300 Gumi-dong Bundang-gu, Seongnam-si, Gyeonggi-do, 13620, South Korea
- Department of Surgery, Seoul National University College of Medicine, Seoul, South Korea
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Nilsen P, Sundemo D, Heintz F, Neher M, Nygren J, Svedberg P, Petersson L. Towards evidence-based practice 2.0: leveraging artificial intelligence in healthcare. FRONTIERS IN HEALTH SERVICES 2024; 4:1368030. [PMID: 38919828 PMCID: PMC11196845 DOI: 10.3389/frhs.2024.1368030] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2024] [Accepted: 05/31/2024] [Indexed: 06/27/2024]
Abstract
Background Evidence-based practice (EBP) involves making clinical decisions based on three sources of information: evidence, clinical experience and patient preferences. Despite popularization of EBP, research has shown that there are many barriers to achieving the goals of the EBP model. The use of artificial intelligence (AI) in healthcare has been proposed as a means to improve clinical decision-making. The aim of this paper was to pinpoint key challenges pertaining to the three pillars of EBP and to investigate the potential of AI in surmounting these challenges and contributing to a more evidence-based healthcare practice. We conducted a selective review of the literature on EBP and the integration of AI in healthcare to achieve this. Challenges with the three components of EBP Clinical decision-making in line with the EBP model presents several challenges. The availability and existence of robust evidence sometimes pose limitations due to slow generation and dissemination processes, as well as the scarcity of high-quality evidence. Direct application of evidence is not always viable because studies often involve patient groups distinct from those encountered in routine healthcare. Clinicians need to rely on their clinical experience to interpret the relevance of evidence and contextualize it within the unique needs of their patients. Moreover, clinical decision-making might be influenced by cognitive and implicit biases. Achieving patient involvement and shared decision-making between clinicians and patients remains challenging in routine healthcare practice due to factors such as low levels of health literacy among patients and their reluctance to actively participate, barriers rooted in clinicians' attitudes, scepticism towards patient knowledge and ineffective communication strategies, busy healthcare environments and limited resources. AI assistance for the three components of EBP AI presents a promising solution to address several challenges inherent in the research process, from conducting studies, generating evidence, synthesizing findings, and disseminating crucial information to clinicians to implementing these findings into routine practice. AI systems have a distinct advantage over human clinicians in processing specific types of data and information. The use of AI has shown great promise in areas such as image analysis. AI presents promising avenues to enhance patient engagement by saving time for clinicians and has the potential to increase patient autonomy although there is a lack of research on this issue. Conclusion This review underscores AI's potential to augment evidence-based healthcare practices, potentially marking the emergence of EBP 2.0. However, there are also uncertainties regarding how AI will contribute to a more evidence-based healthcare. Hence, empirical research is essential to validate and substantiate various aspects of AI use in healthcare.
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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
| | - David Sundemo
- School of Public Health and Community Medicine, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Lerum Närhälsan Primary Healthcare Center, Lerum, Sweden
| | - Fredrik Heintz
- Department of Computer and Information Science, Linköping University, Linköping, Sweden
| | - Margit Neher
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Jens Nygren
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Petra Svedberg
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
| | - Lena Petersson
- School of Health and Welfare, Halmstad University, Halmstad, Sweden
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Li M, Xiong X, Xu B, Dickson C. Chinese Oncologists' Perspectives on Integrating AI into Clinical Practice: Cross-Sectional Survey Study. JMIR Form Res 2024; 8:e53918. [PMID: 38838307 PMCID: PMC11187515 DOI: 10.2196/53918] [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: 10/24/2023] [Revised: 02/21/2024] [Accepted: 04/03/2024] [Indexed: 06/07/2024] Open
Abstract
BACKGROUND The rapid development of artificial intelligence (AI) has brought significant interest to its potential applications in oncology. Although AI-powered tools are already being implemented in some Chinese hospitals, their integration into clinical practice raises several concerns for Chinese oncologists. OBJECTIVE This study aims to explore the concerns of Chinese oncologists regarding the integration of AI into clinical practice and to identify the factors influencing these concerns. METHODS A total of 228 Chinese oncologists participated in a cross-sectional web-based survey from April to June in 2023 in mainland China. The survey gauged their worries about AI with multiple-choice questions. The survey evaluated their views on the statements of "The impact of AI on the doctor-patient relationship" and "AI will replace doctors." The data were analyzed using descriptive statistics, and variate analyses were used to find correlations between the oncologists' backgrounds and their concerns. RESULTS The study revealed that the most prominent concerns were the potential for AI to mislead diagnosis and treatment (163/228, 71.5%); an overreliance on AI (162/228, 71%); data and algorithm bias (123/228, 54%); issues with data security and patient privacy (123/228, 54%); and a lag in the adaptation of laws, regulations, and policies in keeping up with AI's development (115/228, 50.4%). Oncologists with a bachelor's degree expressed heightened concerns related to data and algorithm bias (34/49, 69%; P=.03) and the lagging nature of legal, regulatory, and policy issues (32/49, 65%; P=.046). Regarding AI's impact on doctor-patient relationships, 53.1% (121/228) saw a positive impact, whereas 35.5% (81/228) found it difficult to judge, 9.2% (21/228) feared increased disputes, and 2.2% (5/228) believed that there is no impact. Although sex differences were not significant (P=.08), perceptions varied-male oncologists tended to be more positive than female oncologists (74/135, 54.8% vs 47/93, 50%). Oncologists with a bachelor's degree (26/49, 53%; P=.03) and experienced clinicians (≥21 years; 28/56, 50%; P=.054). found it the hardest to judge. Those with IT experience were significantly more positive (25/35, 71%) than those without (96/193, 49.7%; P=.02). Opinions regarding the possibility of AI replacing doctors were diverse, with 23.2% (53/228) strongly disagreeing, 14% (32/228) disagreeing, 29.8% (68/228) being neutral, 16.2% (37/228) agreeing, and 16.7% (38/228) strongly agreeing. There were no significant correlations with demographic and professional factors (all P>.05). CONCLUSIONS Addressing oncologists' concerns about AI requires collaborative efforts from policy makers, developers, health care professionals, and legal experts. Emphasizing transparency, human-centered design, bias mitigation, and education about AI's potential and limitations is crucial. Through close collaboration and a multidisciplinary strategy, AI can be effectively integrated into oncology, balancing benefits with ethical considerations and enhancing patient care.
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Affiliation(s)
- Ming Li
- Department of Health Policy Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
| | - XiaoMin Xiong
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China
| | - Bo Xu
- Chongqing Key Laboratory of Intelligent Oncology for Breast Cancer, Chongqing University Cancer Hospital, Chongqing University School of Medicine, Chongqing, China
- Department of Biochemistry and Molecular Biology, Key Laboratory of Breast Cancer Prevention and Therapy, Ministry of Education, National Cancer Research Center, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China
| | - Conan Dickson
- Department of Health Policy Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, United States
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Syed W, Babelghaith SD, Al-Arifi MN. Assessment of Saudi Public Perceptions and Opinions towards Artificial Intelligence in Health Care. MEDICINA (KAUNAS, LITHUANIA) 2024; 60:938. [PMID: 38929555 PMCID: PMC11205650 DOI: 10.3390/medicina60060938] [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: 05/01/2024] [Revised: 05/31/2024] [Accepted: 06/02/2024] [Indexed: 06/28/2024]
Abstract
Background and Objectives: The healthcare system in Saudi Arabia is growing rapidly with the utilization of advanced technologies. Therefore, this study aimed to assess the Saudi public perceptions and opinions towards artificial intelligence (AI) in health care. Materials and Methods: This cross-sectional web-based questionnaire study was conducted between January and April 2024. Data were analyzed from 830 participants. The perceptions of the public towards AI were assessed using 21-item questionnaires. Results: Among the respondents, 69.4% were males and 46% of them were aged above 41 years old. A total of 84.1% of the participants knew about AI, while 61.1% of them believed that AI is a tool that helps healthcare professionals, and 12.5% of them thought that AI may replace the physician, pharmacist, or nurse in the healthcare system. With regard to opinion on the widespread use of AI, 45.8% of the study population believed that healthcare professionals will be improved with the widespread use of artificial intelligence. The mean perception score of AI among males was 38.4 (SD = 6.1) and this was found to be higher than for females at 37.7 (SD = 5.3); however, no significant difference was observed (p = 0.072). Similarly, the mean perception score was higher among young adults aged between 20 and 25 years at 38.9 (SD = 6.1) compared to other age groups, but indicating no significant association between them (p = 0.198). Conclusions: The results showed that the Saudi public had a favorable opinion and perceptions of AI in health care. This suggests that health management recommendations should be made regarding how to successfully integrate and use medical AI while maintaining patient safety.
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Affiliation(s)
- Wajid Syed
- Department of Clinical Pharmacy, College of Pharmacy, King Saud University, Riyadh 11451, Saudi Arabia; (S.D.B.); (M.N.A.-A.)
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Scott IA, van der Vegt A, Lane P, McPhail S, Magrabi F. Achieving large-scale clinician adoption of AI-enabled decision support. BMJ Health Care Inform 2024; 31:e100971. [PMID: 38816209 PMCID: PMC11141172 DOI: 10.1136/bmjhci-2023-100971] [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: 11/19/2023] [Accepted: 05/15/2024] [Indexed: 06/01/2024] Open
Abstract
Computerised decision support (CDS) tools enabled by artificial intelligence (AI) seek to enhance accuracy and efficiency of clinician decision-making at the point of care. Statistical models developed using machine learning (ML) underpin most current tools. However, despite thousands of models and hundreds of regulator-approved tools internationally, large-scale uptake into routine clinical practice has proved elusive. While underdeveloped system readiness and investment in AI/ML within Australia and perhaps other countries are impediments, clinician ambivalence towards adopting these tools at scale could be a major inhibitor. We propose a set of principles and several strategic enablers for obtaining broad clinician acceptance of AI/ML-enabled CDS tools.
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Affiliation(s)
- Ian A Scott
- Internal Medicine and Clinical Epidemiology, Princess Alexandra Hospital, Brisbane, Queensland, Australia
- Centre for Health Services Research, The University of Queensland Faculty of Medicine and Biomedical Sciences, Brisbane, Queensland, Australia
| | - Anton van der Vegt
- Digital Health Centre, The University of Queensland Faculty of Medicine and Biomedical Sciences, Herston, Queensland, Australia
| | - Paul Lane
- Safety, Quality and Innovation, The Prince Charles Hospital, Brisbane, Queensland, Australia
| | - Steven McPhail
- Australian Centre for Health Services Innovation, Queensland University of Technology Faculty of Health, Brisbane, Queensland, Australia
| | - Farah Magrabi
- Macquarie University, Sydney, New South Wales, Australia
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Razali HYH, Yusof ANM. Navigating cultural diversity: harnessing AI for mental health diagnosis despite value-laden judgements. JOURNAL OF MEDICAL ETHICS 2024:jme-2024-110086. [PMID: 38802139 DOI: 10.1136/jme-2024-110086] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/15/2024] [Accepted: 05/20/2024] [Indexed: 05/29/2024]
Affiliation(s)
- Hazdalila Yais Haji Razali
- Department of Medical Ethics and Law, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia
| | - Aimi Nadia Mohd Yusof
- Department of Medical Ethics and Law, Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh, Selangor, Malaysia
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Hasan HE, Jaber D, Khabour OF, Alzoubi KH. Ethical considerations and concerns in the implementation of AI in pharmacy practice: a cross-sectional study. BMC Med Ethics 2024; 25:55. [PMID: 38750441 PMCID: PMC11096093 DOI: 10.1186/s12910-024-01062-8] [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: 01/14/2024] [Accepted: 05/09/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Integrating artificial intelligence (AI) into healthcare has raised significant ethical concerns. In pharmacy practice, AI offers promising advances but also poses ethical challenges. METHODS A cross-sectional study was conducted in countries from the Middle East and North Africa (MENA) region on 501 pharmacy professionals. A 12-item online questionnaire assessed ethical concerns related to the adoption of AI in pharmacy practice. Demographic factors associated with ethical concerns were analyzed via SPSS v.27 software using appropriate statistical tests. RESULTS Participants expressed concerns about patient data privacy (58.9%), cybersecurity threats (58.9%), potential job displacement (62.9%), and lack of legal regulation (67.0%). Tech-savviness and basic AI understanding were correlated with higher concern scores (p < 0.001). Ethical implications include the need for informed consent, beneficence, justice, and transparency in the use of AI. CONCLUSION The findings emphasize the importance of ethical guidelines, education, and patient autonomy in adopting AI. Collaboration, data privacy, and equitable access are crucial to the responsible use of AI in pharmacy practice.
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Affiliation(s)
- Hisham E Hasan
- Department of Clinical Pharmacy, Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, 22110, Jordan.
- Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, 13110, Jordan.
| | - Deema Jaber
- Department of Clinical Pharmacy, Faculty of Pharmacy, Zarqa University, Zarqa, 13110, Jordan
| | - Omar F Khabour
- Department of Medical Laboratory Sciences, Faculty of Applied Medical Sciences, Jordan University of Science and Technology, Irbid, 22110, Jordan
| | - Karem H Alzoubi
- Department of Pharmacy Practice and Pharmacotherapeutics, College of Pharmacy, University of Sharjah, Sharjah, 27272, United Arab Emirates
- Faculty of Pharmacy, Jordan University of Science and Technology, Irbid, 22110, Jordan
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Shuaib A. Transforming Healthcare with AI: Promises, Pitfalls, and Pathways Forward. Int J Gen Med 2024; 17:1765-1771. [PMID: 38706749 PMCID: PMC11070153 DOI: 10.2147/ijgm.s449598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2023] [Accepted: 04/17/2024] [Indexed: 05/07/2024] Open
Abstract
This perspective paper provides a comprehensive examination of artificial intelligence (AI) in healthcare, focusing on its transformative impact on clinical practices, decision-making, and physician-patient relationships. By integrating insights from evidence, research, and real-world examples, it offers a balanced analysis of AI's capabilities and limitations, emphasizing its role in streamlining administrative processes, enhancing patient care, and reducing physician burnout while maintaining a human-centric approach in medicine. The research underscores AI's capacity to augment clinical decision-making and improve patient interactions, but it also highlights the variable impact of AI in different healthcare settings. The need for context-specific adaptations and careful integration of AI technologies into existing healthcare workflows is emphasized to maximize benefits and minimize unintended consequences. Significant attention is given to the implications of AI on the roles and competencies of healthcare professionals. The emergence of AI necessitates new skills in data literacy and technology use, prompting a shift in educational curricula towards digital health and AI training. Ethical considerations are a pivotal aspect of the discussion. The paper explores the challenges posed by data privacy concerns, algorithmic biases, and ensuring equitable access to AI-driven healthcare. It advocates for the development of comprehensive ethical frameworks and ongoing research to guide the responsible use of AI in healthcare. Conclusively, the paper advocates for a balanced approach to AI adoption in healthcare, highlighting the importance of ongoing research, strategic implementation, and the synergistic combination of human expertise with AI technologies for optimal patient care.
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Affiliation(s)
- Ali Shuaib
- Biomedical Engineering Unit, Department of Physiology, Faculty of Medicine, Kuwait University, Safat, 13110, Kuwait
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Ahmed AA, Fawi M, Brychcy A, Abouzid M, Witt M, Kaczmarek E. Development and Validation of a Deep Learning Model for Histopathological Slide Analysis in Lung Cancer Diagnosis. Cancers (Basel) 2024; 16:1506. [PMID: 38672588 PMCID: PMC11048051 DOI: 10.3390/cancers16081506] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2024] [Revised: 04/04/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024] Open
Abstract
Lung cancer is the leading cause of cancer-related deaths worldwide. Two of the crucial factors contributing to these fatalities are delayed diagnosis and suboptimal prognosis. The rapid advancement of deep learning (DL) approaches provides a significant opportunity for medical imaging techniques to play a pivotal role in the early detection of lung tumors and subsequent monitoring during treatment. This study presents a DL-based model for efficient lung cancer detection using whole-slide images. Our methodology combines convolutional neural networks (CNNs) and separable CNNs with residual blocks, thereby improving classification performance. Our model improves accuracy (96% to 98%) and robustness in distinguishing between cancerous and non-cancerous lung cell images in less than 10 s. Moreover, the model's overall performance surpassed that of active pathologists, with an accuracy of 100% vs. 79%. There was a significant linear correlation between pathologists' accuracy and years of experience (r Pearson = 0.71, 95% CI 0.14 to 0.93, p = 0.022). We conclude that this model enhances the accuracy of cancer detection and can be used to train junior pathologists.
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Affiliation(s)
- Alhassan Ali Ahmed
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland;
- Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland;
| | - Muhammad Fawi
- Spider Silk Security DMCC, Dubai 282945, United Arab Emirates
| | - Agnieszka Brychcy
- Department of Clinical Patomorphology, Heliodor Swiecicki Clinical Hospital of the Poznan University of Medical Sciences, 61-806 Poznan, Poland
| | - Mohamed Abouzid
- Doctoral School, Poznan University of Medical Sciences, 61-806 Poznan, Poland;
- Department of Physical Pharmacy and Pharmacokinetics, Poznan University of Medical Sciences, 60-806 Poznan, Poland
| | - Martin Witt
- Department of Anatomy, Poznan University of Medical Sciences, 60-806 Poznan, Poland;
- Department of Anatomy, Technische Universität Dresden, 01307 Dresden, Germany
| | - Elżbieta Kaczmarek
- Department of Bioinformatics and Computational Biology, Poznan University of Medical Sciences, 61-806 Poznan, Poland;
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Al-Anazi S, Al-Omari A, Alanazi S, Marar A, Asad M, Alawaji F, Alwateid S. Artificial intelligence in respiratory care: Current scenario and future perspective. Ann Thorac Med 2024; 19:117-130. [PMID: 38766378 PMCID: PMC11100474 DOI: 10.4103/atm.atm_192_23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/13/2023] [Revised: 09/08/2023] [Accepted: 09/11/2023] [Indexed: 05/22/2024] Open
Abstract
BACKGROUND This narrative review aims to explore the current state and future perspective of artificial intelligence (AI) in respiratory care. The objective is to provide insights into the potential impact of AI in this field. METHODS A comprehensive analysis of relevant literature and research studies was conducted to examine the applications of AI in respiratory care and identify areas of advancement. The analysis included studies on remote monitoring, early detection, smart ventilation systems, and collaborative decision-making. RESULTS The obtained results highlight the transformative potential of AI in respiratory care. AI algorithms have shown promising capabilities in enabling tailored treatment plans based on patient-specific data. Remote monitoring using AI-powered devices allows for real-time feedback to health-care providers, enhancing patient care. AI algorithms have also demonstrated the ability to detect respiratory conditions at an early stage, leading to timely interventions and improved outcomes. Moreover, AI can optimize mechanical ventilation through continuous monitoring, enhancing patient comfort and reducing complications. Collaborative AI systems have the potential to augment the expertise of health-care professionals, leading to more accurate diagnoses and effective treatment strategies. CONCLUSION By improving diagnosis, AI has the potential to revolutionize respiratory care, treatment planning, and patient monitoring. While challenges and ethical considerations remain, the transformative impact of AI in this domain cannot be overstated. By leveraging the advancements and insights from this narrative review, health-care professionals and researchers can continue to harness the power of AI to improve patient outcomes and enhance respiratory care practices. IMPROVEMENTS Based on the findings, future research should focus on refining AI algorithms to enhance their accuracy, reliability, and interpretability. In addition, attention should be given to addressing ethical considerations, ensuring data privacy, and establishing regulatory frameworks to govern the responsible implementation of AI in respiratory care.
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Affiliation(s)
- Saad Al-Anazi
- Lead Clincial Appliaction AzeerTrade (Lowenstein Medical Company), Riyadh, Saudi Arabia
| | - Awad Al-Omari
- Department of Intensive Care, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Safug Alanazi
- Intensivist, Al Hammadi Hospital, Riyadh, Saudi Arabia
| | - Aqeelah Marar
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
| | - Mohammed Asad
- Department of Emergency, Dr. Sulaiman Al-Habib Group Hospitals, Riyadh, Saudi Arabia
| | - Fadi Alawaji
- Ar Rass General Hospital, Qassim Health Cluster, Senior Laboratory Specialist, Rass Region, Qassim City, Saudi Arabia
| | - Salman Alwateid
- Respiratory Care Administration, King Fahad Medical City, Riyadh, Saudi Arabia
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Lysø EH, Hesjedal MB, Skolbekken JA, Solbjør M. Men's sociotechnical imaginaries of artificial intelligence for prostate cancer diagnostics - A focus group study. Soc Sci Med 2024; 347:116771. [PMID: 38537333 DOI: 10.1016/j.socscimed.2024.116771] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 03/05/2024] [Accepted: 03/08/2024] [Indexed: 04/20/2024]
Abstract
Artificial intelligence (AI) is increasingly used for diagnostic purposes in cancer care. Prostate cancer is one of the most prevalent cancers affecting men worldwide, but current diagnostic approaches have limitations in terms of specificity and sensitivity. Using AI to interpret MR images in prostate cancer diagnostics shows promising results, but raises questions about implementation, user acceptance, trust, and doctor-patient communication. Drawing on approaches from the sociology of expectations and theories about sociotechnical imaginaries, we explore men's expectations of artificial intelligence for prostate cancer diagnostics. We conducted ten focus groups with 48 men aged 54-85 in Norway with various experiences of prostate cancer diagnostics. Five groups of men had been treated for prostate cancer, one group was on active surveillance, two groups had been through prostate cancer diagnostics without having a diagnosis, and two groups of men had no experience with prostate cancer diagnostics or treatment. Data was subject to reflexive thematic analysis. Our analysis suggests that men's expectations of AI for prostate cancer diagnostics come from two perspectives: Technology-centered expectations that build on their conceptions of AI's form and agency, and human-centered expectations of AI that build on their perceptions of patient-professional relationships and decision-making processes. These two perspectives are intertwined in three imaginaries of AI: The tool imaginary, the advanced machine imaginary, and the intelligence imaginary - each carrying distinct expectations and ideas of technologies and humans' role in decision-making processes. These expectations are multifaceted and simultaneously optimistic and pessimistic; while AI is expected to improve the accuracy of cancer diagnoses and facilitate more personalized medicine, AI is also expected to threaten interpersonal and communicational relationships between patients and healthcare professionals, and the maintenance of trust in these relationships. This emphasizes how AI cannot be implemented without caution about maintaining human healthcare relationships.
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Affiliation(s)
- Emilie Hybertsen Lysø
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway.
| | - Maria Bårdsen Hesjedal
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
| | - John-Arne Skolbekken
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
| | - Marit Solbjør
- Norwegian University of Science and Technology, Department of Public Health and Nursing, Håkon Jarls gate 11, 7030, Trondheim, Norway
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Alshanberi AM, Mousa AH, Hashim SA, Almutairi RS, Alrehali S, Hamisu AM, Shaikhomer M, Ansari SA. Knowledge and Perception of Artificial Intelligence among Faculty Members and Students at Batterjee Medical College. JOURNAL OF PHARMACY AND BIOALLIED SCIENCES 2024; 16:S1815-S1820. [PMID: 38882896 PMCID: PMC11174240 DOI: 10.4103/jpbs.jpbs_1162_23] [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: 11/13/2023] [Revised: 01/21/2024] [Accepted: 02/01/2024] [Indexed: 06/18/2024] Open
Abstract
Background Mounting research suggests that artificial intelligence (AI) is one of the innovations that aid in the patient's diagnosis and treatment, but unfortunately limited research has been conducted in this regard in the Kingdom of Saudi Arabia (KSA). Hence, this study aimed to assess the level of knowledge and awareness of AI among faculty members and medicine students in one of the premier medical colleges in KSA. Methods A cross-sectional descriptive study was conducted at Batterjee Medical College (BMC), Jeddah (KSA), from November 2022 to April 2023. Result A total of 131 participants contributed to our study, of which three were excluded due to incomplete responses, thereby giving a response rate of 98%. Conclusion 85.4% of the respondents believe that AI has a positive impact on the healthcare system and physicians in general. Hence, there should be a mandatory course in medical schools that can prepare future doctors to diagnose patients more accurately, make predictions about patients' future health, and recommend better treatments.
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Affiliation(s)
- Asim M Alshanberi
- Department of Community Medicine and Pilgrims Health Care, Umm Al-Qura University, Makkah, Saudi Arabia
| | - Ahmed H Mousa
- Department of Neurosurgery, Graduate Medical Education, Mohammed Bin Rashid University (MBRU), Dubai Health, Dubai, United Arab Emirates
- Department of Neurosurgery, Rashid Hospital, Dubai Health, Dubai, United Arab Emirates
- General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Sama A Hashim
- General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Reem S Almutairi
- General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Sara Alrehali
- General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Aisha M Hamisu
- General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
| | - Mohammed Shaikhomer
- Department of Internal Medicine, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Shakeel A Ansari
- Department of Biochemistry, General Medicine Practice Program, Batterjee Medical College, Jeddah, Saudi Arabia
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Giannitto C, Carnicelli G, Lusi S, Ammirabile A, Casiraghi E, De Virgilio A, Esposito AA, Farina D, Ferreli F, Franzese C, Frigerio GM, Lo Casto A, Malvezzi L, Lorini L, Othman AE, Preda L, Scorsetti M, Bossi P, Mercante G, Spriano G, Balzarini L, Francone M. The Use of Artificial Intelligence in Head and Neck Cancers: A Multidisciplinary Survey. J Pers Med 2024; 14:341. [PMID: 38672968 PMCID: PMC11050769 DOI: 10.3390/jpm14040341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/19/2024] [Accepted: 03/21/2024] [Indexed: 04/28/2024] Open
Abstract
Artificial intelligence (AI) approaches have been introduced in various disciplines but remain rather unused in head and neck (H&N) cancers. This survey aimed to infer the current applications of and attitudes toward AI in the multidisciplinary care of H&N cancers. From November 2020 to June 2022, a web-based questionnaire examining the relationship between AI usage and professionals' demographics and attitudes was delivered to different professionals involved in H&N cancers through social media and mailing lists. A total of 139 professionals completed the questionnaire. Only 49.7% of the respondents reported having experience with AI. The most frequent AI users were radiologists (66.2%). Significant predictors of AI use were primary specialty (V = 0.455; p < 0.001), academic qualification and age. AI's potential was seen in the improvement of diagnostic accuracy (72%), surgical planning (64.7%), treatment selection (57.6%), risk assessment (50.4%) and the prediction of complications (45.3%). Among participants, 42.7% had significant concerns over AI use, with the most frequent being the 'loss of control' (27.6%) and 'diagnostic errors' (57.0%). This survey reveals limited engagement with AI in multidisciplinary H&N cancer care, highlighting the need for broader implementation and further studies to explore its acceptance and benefits.
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Affiliation(s)
- Caterina Giannitto
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giorgia Carnicelli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Stefano Lusi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Angela Ammirabile
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Elena Casiraghi
- Department of Computer Science “Giovanni degli Antoni”, University of Milan, Via Celoria 18, 20133 Milan, Italy;
- Environmental Genomics and Systems Biology Division, Lawrence Berkeley National Laboratory, 717 Potter Street, Berkeley, CA 94710, USA
| | - Armando De Virgilio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | | | - Davide Farina
- Department of Medical and Surgical Specialties, Radiological Sciences and Public Health, University of Brescia ASST Spedali Civili of Brescia, 25123 Brescia, Italy;
| | - Fabio Ferreli
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ciro Franzese
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Gian Marco Frigerio
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Antonio Lo Casto
- Department of Biomedicine, Neuroscience and Advanced Diagnostics (BIND), University Hospital of Palermo, 90127 Palermo, Italy;
| | - Luca Malvezzi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luigi Lorini
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Medical Oncology and Hematology Unit IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Ahmed E. Othman
- Department of Neuroradiology, University Medical Center Mainz, 55131 Mainz, Germany;
| | - Lorenzo Preda
- Radiology Unit, Department of Clinical, Surgical, Diagnostic and Pediatric Sciences, University of Pavia, 27100 Pavia, Italy;
| | - Marta Scorsetti
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Radiotherapy and Radiosurgery IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Paolo Bossi
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Mercante
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Giuseppe Spriano
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Otorhinolaryngology Unit, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Luca Balzarini
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
| | - Marco Francone
- Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, 20072 Milan, Italy (G.M.F.); (L.L.); (P.B.)
- Department of Diagnostic and Interventional Radiology, IRCCS Humanitas Research Hospital, Via Manzoni 56, 20089 Milan, Italy
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Rosen R. How is technology changing clinician-patient relationships? BMJ 2024; 384:q574. [PMID: 38458641 DOI: 10.1136/bmj.q574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/10/2024]
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Gurnani B, Kaur K. Leveraging ChatGPT for ophthalmic education: A critical appraisal. Eur J Ophthalmol 2024; 34:323-327. [PMID: 37974429 DOI: 10.1177/11206721231215862] [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: 11/19/2023]
Abstract
In recent years, the advent of artificial intelligence (AI) has transformed many sectors, including medical education. This editorial critically appraises the integration of ChatGPT, a state-of-the-art AI language model, into ophthalmic education, focusing on its potential, limitations, and ethical considerations. The application of ChatGPT in teaching and training ophthalmologists presents an innovative method to offer real-time, customized learning experiences. Through a systematic analysis of both experimental and clinical data, this editorial examines how ChatGPT enhances engagement, understanding, and retention of complex ophthalmological concepts. The study also evaluates the efficacy of ChatGPT in simulating patient interactions and clinical scenarios, which can foster improved diagnostic and interpersonal skills. Despite the promising advantages, concerns regarding reliability, lack of personal touch, and potential biases in the AI-generated content are scrutinized. Ethical considerations concerning data privacy and potential misuse are also explored. The findings underline the need for carefully designed integration, continuous evaluation, and adherence to ethical guidelines to maximize benefits while mitigating risks. By shedding light on these multifaceted aspects, this paper contributes to the ongoing discourse on the incorporation of AI in medical education, offering valuable insights and guidance for educators, practitioners, and policymakers aiming to leverage modern technology for enhancing ophthalmic education.
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Affiliation(s)
- Bharat Gurnani
- Cataract, Cornea, Trauma, External Diseases, Ocular Surface and Refractive Services, ASG Eye Hospital, Jodhpur, Rajasthan, India
- Sadguru Netra Chikitsalya, Shri Sadguru Seva Sangh Trust, Chitrakoot, Madhya Pradesh, India
| | - Kirandeep Kaur
- Cataract, Pediatric Ophthalmology and Strabismus, ASG Eye Hospital, Jodhpur, Rajasthan, India
- Children Eye Care Centre, Sadguru Netra Chikitsalya, Shri Sadguru Seva Sangh Trust, Chitrakoot, Madhya Pradesh, India
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Hussain A, Lee C, Hu E, Amirouche F. Deep learning automation of radiographic patterns for hallux valgus diagnosis. World J Orthop 2024; 15:105-109. [PMID: 38464350 PMCID: PMC10921175 DOI: 10.5312/wjo.v15.i2.105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Revised: 12/22/2023] [Accepted: 01/04/2024] [Indexed: 02/07/2024] Open
Abstract
Artificial intelligence (AI) and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine. Deep learning has already been utilized for automated detection of pneumonia from chest radiographs, diabetic retinopathy, breast cancer, skin carcinoma classification, and metastatic lymphadenopathy detection, with diagnostic reliability akin to medical experts. In the World Journal of Orthopedics article, the authors apply an automated and AI-assisted technique to determine the hallux valgus angle (HVA) for assessing HV foot deformity. With the U-net neural network, the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior high-resolution radiographs. The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability. The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency. Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies, this work adds to the growing evidence supporting AI as a powerful diagnostic tool.
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Affiliation(s)
- Angela Hussain
- Department of Orthopaedic Surgery, University of Illinois College of Medicine, Chicago, IL 60612, United States
| | - Cadence Lee
- Department of Orthopaedic Surgery, University of Illinois College of Medicine, Chicago, IL 60612, United States
| | - Eric Hu
- Department of Orthopaedic Surgery, University of Illinois College of Medicine, Chicago, IL 60612, United States
| | - Farid Amirouche
- Department of Orthopaedics Surgery, University of Illinois at Chicago, Chicago, IL 60612, United States
- Department of Orthopaedic Surgery, Northshore University Health System, Skokie, IL 6007, United States
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Barwise AK, Curtis S, Diedrich DA, Pickering BW. Using artificial intelligence to promote equitable care for inpatients with language barriers and complex medical needs: clinical stakeholder perspectives. J Am Med Inform Assoc 2024; 31:611-621. [PMID: 38099504 PMCID: PMC10873784 DOI: 10.1093/jamia/ocad224] [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: 06/23/2023] [Accepted: 11/14/2023] [Indexed: 02/18/2024] Open
Abstract
OBJECTIVES Inpatients with language barriers and complex medical needs suffer disparities in quality of care, safety, and health outcomes. Although in-person interpreters are particularly beneficial for these patients, they are underused. We plan to use machine learning predictive analytics to reliably identify patients with language barriers and complex medical needs to prioritize them for in-person interpreters. MATERIALS AND METHODS This qualitative study used stakeholder engagement through semi-structured interviews to understand the perceived risks and benefits of artificial intelligence (AI) in this domain. Stakeholders included clinicians, interpreters, and personnel involved in caring for these patients or for organizing interpreters. Data were coded and analyzed using NVIVO software. RESULTS We completed 49 interviews. Key perceived risks included concerns about transparency, accuracy, redundancy, privacy, perceived stigmatization among patients, alert fatigue, and supply-demand issues. Key perceived benefits included increased awareness of in-person interpreters, improved standard of care and prioritization for interpreter utilization; a streamlined process for accessing interpreters, empowered clinicians, and potential to overcome clinician bias. DISCUSSION This is the first study that elicits stakeholder perspectives on the use of AI with the goal of improved clinical care for patients with language barriers. Perceived benefits and risks related to the use of AI in this domain, overlapped with known hazards and values of AI but some benefits were unique for addressing challenges with providing interpreter services to patients with language barriers. CONCLUSION Artificial intelligence to identify and prioritize patients for interpreter services has the potential to improve standard of care and address healthcare disparities among patients with language barriers.
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Affiliation(s)
- Amelia K Barwise
- Biomedical Ethics Research Program, Pulmonary and Critical Care Medicine, Mayo Clinic, Rochester, MN 55902, United States
| | - Susan Curtis
- Biomedical Ethics Research Program, Mayo Clinic, Rochester, MN 55902, United States
| | - Daniel A Diedrich
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55902, United States
| | - Brian W Pickering
- Department of Anesthesiology and Perioperative Medicine, Mayo Clinic, Rochester, MN 55902, United States
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Green BL, Murphy A, Robinson E. Accelerating health disparities research with artificial intelligence. Front Digit Health 2024; 6:1330160. [PMID: 38322109 PMCID: PMC10844447 DOI: 10.3389/fdgth.2024.1330160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024] Open
Affiliation(s)
- B. Lee Green
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States
| | - Anastasia Murphy
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States
| | - Edmondo Robinson
- Center for Digital Health, Moffitt Cancer Center, Tampa, FL, United States
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Weidener L, Fischer M. Role of Ethics in Developing AI-Based Applications in Medicine: Insights From Expert Interviews and Discussion of Implications. JMIR AI 2024; 3:e51204. [PMID: 38875585 PMCID: PMC11041491 DOI: 10.2196/51204] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/24/2023] [Revised: 11/20/2023] [Accepted: 12/09/2023] [Indexed: 06/16/2024]
Abstract
BACKGROUND The integration of artificial intelligence (AI)-based applications in the medical field has increased significantly, offering potential improvements in patient care and diagnostics. However, alongside these advancements, there is growing concern about ethical considerations, such as bias, informed consent, and trust in the development of these technologies. OBJECTIVE This study aims to assess the role of ethics in the development of AI-based applications in medicine. Furthermore, this study focuses on the potential consequences of neglecting ethical considerations in AI development, particularly their impact on patients and physicians. METHODS Qualitative content analysis was used to analyze the responses from expert interviews. Experts were selected based on their involvement in the research or practical development of AI-based applications in medicine for at least 5 years, leading to the inclusion of 7 experts in the study. RESULTS The analysis revealed 3 main categories and 7 subcategories reflecting a wide range of views on the role of ethics in AI development. This variance underscores the subjectivity and complexity of integrating ethics into the development of AI in medicine. Although some experts view ethics as fundamental, others prioritize performance and efficiency, with some perceiving ethics as potential obstacles to technological progress. This dichotomy of perspectives clearly emphasizes the subjectivity and complexity surrounding the role of ethics in AI development, reflecting the inherent multifaceted nature of this issue. CONCLUSIONS Despite the methodological limitations impacting the generalizability of the results, this study underscores the critical importance of consistent and integrated ethical considerations in AI development for medical applications. It advocates further research into effective strategies for ethical AI development, emphasizing the need for transparent and responsible practices, consideration of diverse data sources, physician training, and the establishment of comprehensive ethical and legal frameworks.
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Affiliation(s)
- Lukas Weidener
- Research Unit for Quality and Ethics in Health Care, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
| | - Michael Fischer
- Research Unit for Quality and Ethics in Health Care, UMIT TIROL - Private University for Health Sciences and Health Technology, Hall in Tirol, Austria
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Li W, Ge X, Liu S, Xu L, Zhai X, Yu L. Opportunities and challenges of traditional Chinese medicine doctors in the era of artificial intelligence. Front Med (Lausanne) 2024; 10:1336175. [PMID: 38274445 PMCID: PMC10808796 DOI: 10.3389/fmed.2023.1336175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Accepted: 12/27/2023] [Indexed: 01/27/2024] Open
Abstract
With the exponential advancement of artificial intelligence (AI) technology, the realm of medicine is experiencing a paradigm shift, engendering a multitude of prospects and trials for healthcare practitioners, encompassing those devoted to the practice of traditional Chinese medicine (TCM). This study explores the evolving landscape for TCM practitioners in the AI era, emphasizing that while AI can be helpful, it cannot replace the role of TCM practitioners. It is paramount to underscore the intrinsic worth of human expertise, accentuating that artificial intelligence (AI) is merely an instrument. On the one hand, AI-enabled tools like intelligent symptom checkers, diagnostic assistance systems, and personalized treatment plans can augment TCM practitioners' expertise and capacity, improving diagnosis accuracy and treatment efficacy. AI-empowered collaborations between Western medicine and TCM can strengthen holistic care. On the other hand, AI may disrupt conventional TCM workflow and doctor-patient relationships. Maintaining the humanistic spirit of TCM while embracing AI requires upholding professional ethics and establishing appropriate regulations. To leverage AI while retaining the essence of TCM, practitioners need to hone holistic analytical skills and see AI as complementary. By highlighting promising applications and potential risks of AI in TCM, this study provides strategic insights for stakeholders to promote the integrated development of AI and TCM for better patient outcomes. With proper implementation, AI can become a valuable assistant for TCM practitioners to elevate healthcare quality.
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Affiliation(s)
- Wenyu Li
- School of Marxism, Capital Normal University, Beijing, China
| | - Xiaolei Ge
- Wangjing Hospital of China Academy of Traditional Chinese Medicine, Beijing, China
| | - Shuai Liu
- Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Lili Xu
- Graduate School of Chinese Academy of Traditional Chinese Medicine, Beijing, China
| | - Xu Zhai
- Wangjing Hospital of China Academy of Traditional Chinese Medicine, Beijing, China
| | - Linyong Yu
- China Academy of Chinese Medical Sciences, Beijing, China
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Rony MKK, Parvin MR, Wahiduzzaman M, Debnath M, Bala SD, Kayesh I. "I Wonder if my Years of Training and Expertise Will be Devalued by Machines": Concerns About the Replacement of Medical Professionals by Artificial Intelligence. SAGE Open Nurs 2024; 10:23779608241245220. [PMID: 38596508 PMCID: PMC11003342 DOI: 10.1177/23779608241245220] [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: 11/10/2023] [Revised: 03/08/2024] [Accepted: 03/15/2024] [Indexed: 04/11/2024] Open
Abstract
Background The rapid integration of artificial intelligence (AI) into healthcare has raised concerns among healthcare professionals about the potential displacement of human medical professionals by AI technologies. However, the apprehensions and perspectives of healthcare workers regarding the potential substitution of them with AI are unknown. Objective This qualitative research aimed to investigate healthcare workers' concerns about artificial intelligence replacing medical professionals. Methods A descriptive and exploratory research design was employed, drawing upon the Technology Acceptance Model (TAM), Technology Threat Avoidance Theory, and Sociotechnical Systems Theory as theoretical frameworks. Participants were purposively sampled from various healthcare settings, representing a diverse range of roles and backgrounds. Data were collected through individual interviews and focus group discussions, followed by thematic analysis. Results The analysis revealed seven key themes reflecting healthcare workers' concerns, including job security and economic concerns; trust and acceptance of AI; ethical and moral dilemmas; quality of patient care; workforce role redefinition and training; patient-provider relationships; healthcare policy and regulation. Conclusions This research underscores the multifaceted concerns of healthcare workers regarding the increasing role of AI in healthcare. Addressing job security, fostering trust, addressing ethical dilemmas, and redefining workforce roles are crucial factors to consider in the successful integration of AI into healthcare. Healthcare policy and regulation must be developed to guide this transformation while maintaining the quality of patient care and preserving patient-provider relationships. The study findings offer insights for policymakers and healthcare institutions to navigate the evolving landscape of AI in healthcare while addressing the concerns of healthcare professionals.
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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
| | - Mst. Rina Parvin
- Armed Forces Nursing Service, Major at Bangladesh Army (AFNS Officer), Combined Military Hospital, Dhaka, Bangladesh
| | - Md. Wahiduzzaman
- School of Medical Sciences, Shahjalal University of Science and Technology, Sylhet, Bangladesh
| | - Mitun Debnath
- Master of Public Health, National Institute of Preventive and Social Medicine, Dhaka, Bangladesh
| | - Shuvashish Das Bala
- College of Nursing, International University of Business Agriculture and Technology, Dhaka, Bangladesh
| | - Ibne Kayesh
- Institute of Social Welfare and Research, University of Dhaka, Dhaka, Bangladesh
- Faculty of Graduate Studies, University of Kelaniya, Colombo, Sri Lanka
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Singam A. Revolutionizing Patient Care: A Comprehensive Review of Artificial Intelligence Applications in Anesthesia. Cureus 2023; 15:e49887. [PMID: 38174199 PMCID: PMC10762564 DOI: 10.7759/cureus.49887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2023] [Accepted: 12/03/2023] [Indexed: 01/05/2024] Open
Abstract
This review explores the intersection of artificial intelligence (AI) and anesthesia, examining its transformative impact on patient care across various phases. Beginning with a historical overview of anesthesia, we highlight the critical role of technological advancements in ensuring optimal patient outcomes. The emergence of AI in healthcare sets the stage for a comprehensive analysis of its applications in anesthesia. In the preoperative phase, AI facilitates personalized risk assessments and decision support, optimizing anesthesia planning and drug dosage predictions. Moving to the intraoperative phase, we delve into AI's role in monitoring and control through sophisticated anesthesia monitoring and closed-loop systems. Additionally, we discuss the integration of robotics and AI-guided procedures, revolutionizing surgical assistance. Transitioning to the postoperative phase, we explore AI-driven postoperative monitoring, predictive analysis for complications, and the integration of AI into rehabilitation programs and long-term follow-up. These new applications redefine patient recovery, emphasizing personalized care and proactive interventions. However, the integration of AI in anesthesia poses challenges and ethical considerations. Data security, interpretability, and bias in AI algorithms demand scrutiny. Moreover, the evolving patient-doctor relationship in an AI-driven care landscape requires a delicate balance between efficiency and human touch. Looking forward, we discuss the future directions of AI in anesthesia, anticipating advances in technology and AI algorithms. The integration of AI into routine clinical practice and its potential impact on anesthesia education and training are explored, emphasizing the need for collaboration, education, and ethical guidelines. This review provides a comprehensive overview of AI applications in anesthesia, offering insights into the present landscape, challenges, and future directions. The synthesis of historical perspectives, current applications, and future possibilities underscores the transformative potential of AI in revolutionizing patient care within the dynamic field of anesthesia.
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Affiliation(s)
- Amol Singam
- Critical Care Medicine, Jawaharlal Nehru Medical College, Datta Meghe Institute of Higher Education and Research, Wardha, IND
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Tan S, Stretton B, Lee YM, Gupta A, Kovoor J, Bacchi S. More time per patient or more patients per unit time? Emerg Med Australas 2023; 35:885-886. [PMID: 37580089 DOI: 10.1111/1742-6723.14295] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Accepted: 08/02/2023] [Indexed: 08/16/2023]
Affiliation(s)
- Sheryn Tan
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
- The University of Adelaide, Adelaide, South Australia, Australia
| | - Brandon Stretton
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
- The University of Adelaide, Adelaide, South Australia, Australia
| | - Yong Min Lee
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
| | - Aashray Gupta
- The University of Adelaide, Adelaide, South Australia, Australia
- Gold Coast University Hospital, Gold Coast, Queensland, Australia
| | - Joshua Kovoor
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
- The University of Adelaide, Adelaide, South Australia, Australia
| | - Stephen Bacchi
- Royal Adelaide Hospital, Adelaide, South Australia, Australia
- Flinders University, Adelaide, South Australia, Australia
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Doarn CR. Artificial Intelligence and Health Care: What's to Worry? Telemed J E Health 2023; 29:793-794. [PMID: 37267588 DOI: 10.1089/tmj.2023.29095.editorial] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/04/2023] Open
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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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