Exploring stakeholder attitudes towards AI in clinical practice
Open accessAbstract
Objectives: Different stakeholders may hold varying attitudes towards artificial intelligence (AI) applications in healthcare, which may constrain their acceptance if AI developers fail to take them into account. We set out to ascertain evidence of the attitudes of clinicians, consumers, managers, researchers, regulators and industry towards AI applications in healthcare. Methods: We undertook an exploratory analysis of articles whose titles or abstracts contained the terms ‘artificial intelligence’ or ‘AI’ and ‘medical’ or ‘healthcare’ and ‘attitudes’, ‘perceptions’, ‘opinions’, ‘views’, ‘expectations’. Using a snowballing strategy, we searched PubMed and Google Scholar for articles published 1 January 2010 through 31 May 2021. We selected articles relating to non-robotic clinician-facing AI applications used to support healthcare-related tasks or decision-making. Results: Across 27 studies, attitudes towards AI applications in healthcare, in general, were positive, more so for those with direct experience of AI, but provided certain safeguards were met. AI applications which automated data interpretation and synthesis were regarded more favourably by clinicians and consumers than those that directly influenced clinical decisions or potentially impacted clinician–patient relationships. Privacy breaches and personal liability for AI-related error worried clinicians, while loss of clinician oversight and inability to fully share in decision-making worried consumers. Both clinicians and consumers wanted AI-generated advice to be trustworthy, while industry groups emphasised AI benefits and wanted more data, funding and regulatory certainty. Discussion: Certain expectations of AI applications were common to many stakeholder groups from which a set of dependencies can be defined. Conclusion: Stakeholders differ in some but not all of their attitudes towards AI. Those developing and implementing applications should consider policies and processes that bridge attitudinal disconnects between different stakeholders.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Our respondents regarded the technology with skepticism, similar to others.
“Similar to others, they also regarded the technology with skepticism [35,41,42].”
From An Assessment of How Clinicians and Staff Members Use a Diabetes Artificial Intelligence Prediction Tool: Mixed Methods Study (2023) · cites this paper as [42]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Eysenbach, G. (2004) · Journal of Medical Internet Research · doi:10.2196/jmir.6.3.e34 · Cited by 6,480 · see citing works
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579 · see citing works
Algorithm aversion: People erroneously avoid algorithms after seeing them err.
Dietvorst, B. J., Simmons, J. P., & Massey, C. (2015) · Journal of Experimental Psychology: General · doi:10.1037/xge0000033 · Cited by 3,232 · see citing works
Resistance to Medical Artificial Intelligence
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019) · Journal of Consumer Research · doi:10.1093/jcr/ucz013 · Cited by 2,015 · see citing works
Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338 · see citing works
Medical students' attitude towards artificial intelligence: a multicentre survey
Pinto dos Santos, D., Giese, D., Brodehl, S., Chon, S. H., Staab, W., Kleinert, R., Maintz, D., & Baeßler, B. (2019) · European Radiology · doi:10.1007/s00330-018-5601-1 · Cited by 754 · see citing works
Potential liability for physicians using artificial intelligence
Price, W. N., Gerke, S., & Cohen, I. G. (2019) · JAMA · doi:10.1001/jama.2019.15064 · Cited by 579 · see citing works
Sit, C., Srinivasan, R., Amlani, A., Muthuswamy, K., Azam, A., Monzon, L., & Poon, D. S. (2020) · Insights into Imaging · doi:10.1186/s13244-019-0830-7 · Cited by 507 · see citing works
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives
Esmaeilzadeh, P. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01191-1 · Cited by 507 · see citing works
Khosravi, P., Kazemi, E. M., Zhan, Q., Malmsten, J. E., Toschi, M., Zisimopoulos, P., Sigaras, A., Lavery, S. A., Cooper, L., Hickman, C. F. L., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0096-y · Cited by 472 · see citing works
Artificial intelligence and the implementation challenge
Shaw, J., Rudzicz, F., Jamieson, T., & Goldfarb, A. (2019) · Journal of Medical Internet Research · doi:10.2196/13659 · Cited by 389 · see citing works
Physician Confidence in Artificial Intelligence: An Online Mobile Survey
Oh, S., Kim, J. H., Choi, S.-W., Lee, H. J., Hong, J., & Kwon, S. H. (2019) · Journal of Medical Internet Research · doi:10.2196/12422 · Cited by 387 · see citing works
Gerke, S., Babic, B., Evgeniou, T., & Cohen, I. G. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0262-2 · Cited by 359 · see citing works
Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2020) · Annals of Operations Research · doi:10.1007/s10479-018-2818-y · Cited by 346 · see citing works
Blease, C., Kaptchuk, T. J., Bernstein, M. H., Mandl, K. D., Halamka, J. D., & DesRoches, C. M. (2019) · Journal of Medical Internet Research · doi:10.2196/12802 · Cited by 346 · see citing works
Gong, B., Nugent, J. P., Guest, W., Parker, W., Chang, P. J., Khosa, F., & Nicolaou, S. (2019) · Academic Radiology · doi:10.1016/j.acra.2018.10.007 · Cited by 325 · see citing works
Perceptions of Artificial Intelligence Among Healthcare Staff: A Qualitative Survey Study
Castagno, S., & Khalifa, M. (2020) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2020.578983 · Cited by 304 · see citing works
Physician perspectives on integration of artificial intelligence into diagnostic pathology
Sarwar, S., Dent, A., Faust, K., Richer, M., Djuric, U., Van Ommeren, R., & Diamandis, P. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0106-0 · Cited by 297 · see citing works
Laï, M.-C., Brian, M., & Mamzer, M.-F. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-019-02204-y · Cited by 294 · see citing works
Health Care Employees’ Perceptions of the Use of Artificial Intelligence Applications: Survey Study
Abdullah, R., & Fakieh, B. (2020) · Journal of Medical Internet Research · doi:10.2196/17620 · Cited by 286 · see citing works
Nelson, C. A., Pérez-Chada, L. M., Creadore, A., Li, S. J., Lo, K., Manjaly, P., Pournamdari, A. B., Tkachenko, E., Barbieri, J. S., Ko, J. M., et al. (2020) · JAMA Dermatology · doi:10.1001/jamadermatol.2019.5014 · Cited by 278 · see citing works
Scheetz, J., Rothschild, P., McGuinness, M. B., Hadoux, X., Soyer, H. P., Janda, M., Condon, J. J. J., Oakden‐Rayner, L., Palmer, L. J., Keel, S., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-84698-5 · Cited by 277 · see citing works
Patients’ views of wearable devices and AI in healthcare: findings from the ComPaRe e-cohort
Tran, V.-T., Riveros, C., & Ravaud, P. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0132-y · Cited by 266 · see citing works
Artificial intelligence and the future of psychiatry: Insights from a global physician survey
Doraiswamy, P. M., Blease, C., & Bodner, K. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2019.101753 · Cited by 253 · see citing works
My Friends, Editors, Algorithms, and I
Thurman, N. J., Moeller, J., Helberger, N., & Trilling, D. (2018) · Digital Journalism · doi:10.1080/21670811.2018.1493936 · Cited by 249 · see citing works
Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril
Series, T. L. H. S., & National Academy of Medicine (2019) · doi:10.17226/27111 · Cited by 227 · see citing works
Impact of the rise of artificial intelligence in radiology: What do radiologists think?
Waymel, Q., Badr, S., Demondion, X., Cotten, A., & Jacques, T. (2019) · Diagnostic and Interventional Imaging · doi:10.1016/j.diii.2019.03.015 · Cited by 194 · see citing works
Coppola, F., Faggioni, L., Regge, D., Giovagnoni, A., Golfieri, R., Bibbolino, C., Miele, V., Neri, E., & Grassi, R. (2020) · La radiologia medica · doi:10.1007/s11547-020-01205-y · Cited by 171 · see citing works
Lennartz, S., Dratsch, T., Zopfs, D., Persigehl, T., Maintz, D., Große Hokamp, N., & Pinto dos Santos, D. (2021) · Journal of Medical Internet Research · doi:10.2196/24221 · Cited by 153 · see citing works
Collado‐Mesa, F., Alvarez, E., & Arheart, K. L. (2018) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2017.12.021 · Cited by 136 · see citing works
Attitudes of Patients and Their Relatives Toward Artificial Intelligence in Neurosurgery
Palmisciano, P., Jamjoom, A. A. B., Taylor, D., Stoyanov, D., & Marcus, H. J. (2020) · World Neurosurgery · doi:10.1016/j.wneu.2020.03.029 · Cited by 113 · see citing works
Implications of artificial intelligence for medical education
Rampton, V., Mittelman, M., & Goldhahn, J. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30023-6 · Cited by 112 · see citing works
McCradden, M. D., Baba, A., Saha, A., Ahmad, S., Boparai, K., Fadaiefard, P., & Cusimano, M. D. (2020) · CMAJ Open · doi:10.9778/cmajo.20190151 · Cited by 101 · see citing works
Attitudes towards artificial intelligence within dermatology: an international online survey
Polesie, S., Gillstedt, M., Kittler, H., Lallas, A., Tschandl, P., Zalaudek, I., & Paoli, J. (2020) · British Journal of Dermatology · doi:10.1111/bjd.18875 · Cited by 93 · see citing works
Attitudes Toward Artificial Intelligence Among Radiologists, IT Specialists, and Industry
Jungmann, F., Jorg, T., Hähn, F., dos Santos, D. P., Jungmann, S. M., Düber, C., Mildenberger, P., & Kloeckner, R. (2020) · Academic Radiology · doi:10.1016/j.acra.2020.04.011 · Cited by 85 · see citing works
Sisk, B. A., Antes, A. L., Burrous, S., & DuBois, J. M. (2020) · Children · doi:10.3390/children7090145 · Cited by 71 · see citing works
Public vs physician views of liability for artificial intelligence in health care
Khullar, D., Casalino, L. P., Qian, Y., Lu, Y., Chang, E., & Aneja, S. (2021) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocab055 · Cited by 60 · see citing works
Clinical Laboratory Employees’ Attitudes Toward Artificial Intelligence
Ardon, O., & Schmidt, R. L. (2020) · Laboratory Medicine · doi:10.1093/labmed/lmaa023 · Cited by 36 · see citing works
Patient commentary: Stop hyping artificial intelligence—patients will always need human doctors
Mittelman, M., Markham, S., & Taylor, M. (2018) · BMJ · doi:10.1136/bmj.k4669 · Cited by 20 · see citing works
Perlmutter, A. S., Benchoufi, M., Ravaud, P., & Tran, V.-T. (2020) · Journal of Medical Internet Research · doi:10.2196/18986 · Cited by 14 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [20]Pega Inc. What consumers really think about AI: a global study. Insights into the minds of consumers to help Businesses reshape their customer engagement strategies. Cambridge, MA: Pega, 2017. Available: https://www.ciosummits.com/what-consumers-really-think-about-ai.pdf
Cited by
Sorted by citations
Grassini, S. (2023) · Frontiers in Psychology · doi:10.3389/fpsyg.2023.1191628 · Cited by 330 · see citing works
Chen, M., Zhang, B., Cai, Z., Seery, S., Gonzalez, M. J., Ali, N. M., Ren, R., Qiao, Y., Xue, P., & Jiang, Y. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2022.990604 · Cited by 258 · see citing works
Fritsch, S. J., Blankenheim, A., Wahl, A., Hetfeld, P., Maaßen, O., Deffge, S., Kunze, J., Rossaint, R., Riedel, M., Marx, G., et al. (2022) · Digital Health · doi:10.1177/20552076221116772 · Cited by 255 · see citing works
Artificial intelligence-based clinical decision support in pediatrics
Ramgopal, S., Sanchez-Pinto, L. N., Horvat, C. M., Carroll, M. S., Luo, Y., & Florin, T. A. (2022) · Pediatric Research · doi:10.1038/s41390-022-02226-1 · Cited by 187 · see citing works
Shaping the future of AI in healthcare through ethics and governance
Bouderhem, R. (2024) · Humanities and Social Sciences Communications · doi:10.1057/s41599-024-02894-w · Cited by 195 · see citing works
Vo, V., Chen, G., Aquino, Y. S. J., Carter, S. M., Nga, Q., & Woode, M. E. (2023) · Social Science & Medicine · doi:10.1016/j.socscimed.2023.116357 · Cited by 172 · see citing works
Self-citationPatient Perspectives on the Use of Artificial Intelligence in Health Care: A Scoping Review
Moy, S., Irannejad, M., Manning, S. J., Farahani, M., Ahmed, Y., Gao, E., Prabhune, R., Lorenz, S., Mirza, R. M., & Klinger, C. A. (2024) · Journal of patient-centered research and reviews · doi:10.17294/2330-0698.2029 · Cited by 113 · see citing works
Mackenzie, S. C., Sainsbury, C. A. R., & Wake, D. J. (2023) · Diabetologia · doi:10.1007/s00125-023-06038-8 · Cited by 97 · see citing works
Fundi, M., Sanusi, I. T., Oyelere, S. S., & Ayere, M. A. (2024) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2024.100412 · Cited by 85 · see citing works
Toro-Tobón, D., Loor-Torres, R., Duran, M., Fan, J. W., Ospina, N. S., Wu, Y., & Brito, J. P. (2023) · Thyroid · doi:10.1089/thy.2023.0132 · Cited by 80 · see citing works
Sullivan, B. A., Beam, K., Vesoulis, Z. A., Aziz, K. B., Husain, A. N., Knake, L. A., Moreira, A. G., Hooven, T. A., Weiss, E. M., Carr, N., et al. (2023) · Journal of Perinatology · doi:10.1038/s41372-023-01848-5 · Cited by 84 · see citing works
Pedro, A. R., Dias, M. B., Laranjo, L., Cunha, A. S., & Cordeiro, J. V. (2023) · PLOS ONE · doi:10.1371/journal.pone.0290613 · Cited by 74 · see citing works
Farah, L., Murris, J., Borget, I., Guilloux, A., Martelli, N., & Katsahian, S. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.02.004 · Cited by 75 · see citing works
Hamedani, Z., Moradi, M., Kalroozi, F., Anari, A. M., Jalalifar, E., Ansari, A., Aski, B. H., Nezamzadeh, M., & Karim, B. (2023) · Health Science Reports · doi:10.1002/hsr2.1543 · Cited by 72 · see citing works
Integrating ethics in AI development: a qualitative study
Arbelaez Ossa, L., Lorenzini, G., Milford, S. R., Shaw, D., Elger, B. S., & Rost, M. (2024) · BMC Medical Ethics · doi:10.1186/s12910-023-01000-0 · Cited by 67 · see citing works
Achieving large-scale clinician adoption of AI-enabled decision support
Scott, I. A., van der Vegt, A., Lane, P., McPhail, S., & Magrabi, F. (2024) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2023-100971 · Cited by 65 · see citing works
Self-citationScipion, C. E. A., Manchester, M., Federman, A. D., Wang, Y., & Arias, J. J. (2025) · BMJ Open · doi:10.1136/bmjopen-2024-092624 · Cited by 78 · see citing works
Stakeholder Perceptions of the Safety and Assurance of Artificial Intelligence in Healthcare
Sujan, M. A., White, S., Habli, I., & Reynolds, N. (2022) · Safety Science · doi:10.1016/j.ssci.2022.105870 · Cited by 58 · see citing works
Couture, V., Roy, M., Dez, E., Laperle, S., & Bélisle‐Pipon, J. (2023) · Journal of Medical Internet Research · doi:10.2196/44357 · Cited by 60 · see citing works
Subramanian, H. V., Canfield, C., & Shank, D. B. (2024) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2024.102780 · Cited by 67 · see citing works
Artificial intelligence and machine learning for clinical pharmacology
Ryan, D. K., Maclean, R., Balston, A. J., Scourfield, A., Shah, A. D., & Ross, J. (2023) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15930 · Cited by 63 · see citing works
Heinrichs, H., Kies, A., Nagel, S. K., & Kiessling, F. (2025) · Journal of Medical Internet Research · doi:10.2196/74187 · Cited by 66 · see citing works
Migdadi, M. K., Oweidat, I. A., Alosta, M. R., Al‐Mugheed, K., Alabdullah, A. A. S., & Abdelaliem, S. M. F. (2024) · Digital Health · doi:10.1177/20552076241301958 · Cited by 61 · see citing works
Expectations for Artificial Intelligence (AI) in Psychiatry
Monteith, S., Glenn, T., Geddes, J. R., Whybrow, P. C., Achtyes, E. D., & Bauer, M. (2022) · Current Psychiatry Reports · doi:10.1007/s11920-022-01378-5 · Cited by 56 · see citing works
Artificial Intelligence and Groups: Effects of Attitudes and Discretion on Collaboration
Bezrukova, K., Griffith, T. L., Spell, C., Rice, V., & Yang, H. E. (2023) · Group & Organization Management · doi:10.1177/10596011231160574 · Cited by 51 · see citing works
Performance of multimodal artificial intelligence chatbots evaluated on clinical oncology cases
Chen, D., Huang, R. S., Jomy, J., Wong, P., Yan, M., Croke, J., Tong, D., Hope, A., Eng, L., & Raman, S. (2024) · JAMA Network Open · doi:10.1001/jamanetworkopen.2024.37711 · Cited by 49 · see citing works
Oluwadiya, K. S., Adeoti, A. O., Agodirin, S. O., Nottidge, T. E., Usman, M. I., Gali, M. B., Onyemaechi, N. O., Ramat, A. M., Adedire, A., & Zakari, L. Y. (2023) · Nigerian Postgraduate Medical Journal · doi:10.4103/npmj.npmj_186_23 · Cited by 51 · see citing works
Neher, M., Petersson, L., Nygren, J., Svedberg, P., Larsson, I., & Nilsén, P. (2023) · Implementation Science Communications · doi:10.1186/s43058-023-00458-8 · Cited by 54 · see citing works
The Future of Artificial Intelligence in Mental Health Nursing Practice: An Integrative Review
Milasan, L. H., & Scott, D. (2025) · International Journal of Mental Health Nursing · doi:10.1111/inm.70003 · Cited by 47 · see citing works
Wang, Y.-C., Chen, T., & Chiu, M.-C. (2023) · Healthcare Analytics · doi:10.1016/j.health.2023.100183 · Cited by 40 · see citing works
Stroud, A. M., Curtis, S., Weir, I. B., Stout, J., Barry, B. A., Bobo, W. V., Athreya, A. P., & Sharp, R. R. (2025) · JMIR Mental Health · doi:10.2196/64414 · Cited by 42 · see citing works
Medical practitioner perspectives on AI in emergency triage
Townsend, B., Plant, K. L., Hodge, J., Ashaolu, O., & Călinescu, R. (2023) · Frontiers in Digital Health · doi:10.3389/fdgth.2023.1297073 · Cited by 37 · see citing works
Attitudes towards artificial intelligence in emergency medicine
Stewart, J. E., Freeman, S., Eroglu, E., Dumitrascu, N., Lü, J., Goudie, A., Sprivulis, P., Akhlaghi, H., Tran, V., Sanfilippo, F. M., et al. (2023) · Emergency Medicine Australasia · doi:10.1111/1742-6723.14345 · Cited by 36 · see citing works
Ethics of artificial intelligence in prenatal and pediatric genomic medicine
Coghlan, S., Gyngell, C., & Vears, D. F. (2023) · Journal of Community Genetics · doi:10.1007/s12687-023-00678-4 · Cited by 38 · see citing works
Vereschak, O., Alizadeh, F., Bailly, G., & Caramiaux, B. (2024) · International Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642018 · Cited by 58 · see citing works
Lammons, W., Silkens, M., Hunter, J., Shah, S. V., & Stavropoulou, C. (2023) · Journal of Medical Internet Research · doi:10.2196/49303 · Cited by 33 · see citing works
Hani, I. B., Alawadi, S., & Mrayyan, N. E. (2024) · Journal of Decision System · doi:10.1080/12460125.2024.2349425 · Cited by 40 · see citing works
Bobrytska, V. I., Krasylnykova, H., Beseda, N. А., Красильников, С., & Skyrda, T. (2024) · International Journal of Learning Teaching and Educational Research · doi:10.26803/ijlter.23.1.20 · Cited by 45 · see citing works
McLean, K. A., Sgrò, A., Brown, L. R., Buijs, L. F., Mountain, K. E., Shaw, C. A., Drake, T. M., Pius, R., Knight, S. R., Fairfield, C. J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01419-8 · Cited by 31 · see citing works
Dean, T., Seecheran, R. V., Badgett, R. G., Zackula, R., & Symons, J. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae100 · Cited by 30 · see citing works
Anticipating artificial intelligence in mammography screening: views of Swedish breast radiologists
Högberg, C., Larsson, S., & Lång, K. (2023) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2022-100712 · Cited by 28 · see citing works
Hallowell, N., Badger, S. J., McKay, F., Kerasidou, A., & Nellåker, C. (2023) · SSM - Qualitative Research in Health · doi:10.1016/j.ssmqr.2023.100240 · Cited by 28 · see citing works
Gould, D. J., Dowsey, M. M., Glanville-Hearst, M., Spelman, T. D., Bailey, J., Choong, P., & Bunzli, S. (2023) · Journal of Medical Internet Research · doi:10.2196/43632 · Cited by 27 · see citing works
He, X., Xi, Z., & Ding, H. (2023) · Journal of Medical Internet Research · doi:10.2196/50342 · Cited by 29 · see citing works
The Use of Artificial Intelligence in Clinical Care: A Values-Based Guide for Shared Decision Making
Macri, R., & Roberts, S. L. (2023) · Current Oncology · doi:10.3390/curroncol30020168 · Cited by 29 · see citing works
Sankar, B. S., Gilliland, D., Rincon, J., Hermjakob, H., Yan, Y., Adam, I., Lemaster, G., Wang, D., Watson, K., Bui, A., et al. (2024) · Bioengineering · doi:10.3390/bioengineering11100984 · Cited by 26 · see citing works
Are Turkish pre-service teachers worried about AI? A study on AI anxiety and digital literacy
Ayduğ, D., & Altınpulluk, H. (2025) · AI & Society · doi:10.1007/s00146-025-02348-0 · Cited by 31 · see citing works
An example of governance for AI in health services from Aotearoa New Zealand
Whittaker, R., Dobson, R., Jin, C. K., Style, R., Jayathissa, P., Hiini, K., Ross, K., Kawamura, K., Muir, P., the Waitematā AI Governance Group, et al. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00882-z · Cited by 29 · see citing works
Ethical and practical challenges of generative AI in healthcare and proposed solutions: a survey
Tung, T., Hasnaeen, S. M. N., & Zhao, X. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1692517 · Cited by 31 · see citing works
Oremulé, B., Saunders, G. H., Kluk, K., d’Elia, A., & Bruce, I. A. (2024) · The Journal of Laryngology & Otology · doi:10.1017/s0022215124000550 · Cited by 24 · see citing works
Giebel, G. D., Raszke, P., Nowak, H., Palmowski, L., Adamzik, M., Heinz, P., Tokic, M. C., Timmesfeld, N., Brunkhorst, F. M., Wasem, J., et al. (2025) · JMIR Medical Informatics · doi:10.2196/69688 · Cited by 29 · see citing works
Insights on the Current State and Future Outlook of AI in Health Care: Expert Interview Study
Hummelsberger, P., Koch, T. K., Rauh, S., Dorn, J., Lermer, E., Raue, M., Hudecek, M. F. C., Schicho, A., Colak, E., Ghassemi, M., et al. (2023) · JMIR AI · doi:10.2196/47353 · Cited by 23 · see citing works
Hamad, N. I., El‐Ashry, A. M., Ibrahim, I. M., & Hassan, E. A. (2024) · Teaching and learning in nursing · doi:10.1016/j.teln.2024.12.017 · Cited by 22 · see citing works
AI in medical diagnosis: A contextualised study of patient motivations and concerns
Hajiheydari, N., Delgosha, M. S., & Saheb, T. (2025) · Social Science & Medicine · doi:10.1016/j.socscimed.2025.117850 · Cited by 21 · see citing works
Negotiating AI(s) Futures: Competing Imaginaries of AI by Stakeholders in the US, China, and Germany
Richter, V., Katzenbach, C., & Zeng, J. (2025) · Journal of Science Communication · doi:10.22323/2.24020208 · Cited by 20 · see citing works
AI-Based EMG Reporting: A Randomized Controlled Trial
Gorenshtein, A., Weisblat, Y., Khateb, M., Kenan, G., Tsirkin, I., Fayn, G., Geller, S., & Shelly, S. (2025) · Journal of Neurology · doi:10.1007/s00415-025-13261-3 · Cited by 22 · see citing works
King, H., Williams, B. J., Treanor, D. E., & Randell, R. (2022) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocac254 · Cited by 22 · see citing works
Neurology education in the era of artificial intelligence
Kedar, S., & Khazanchi, D. (2022) · Current Opinion in Neurology · doi:10.1097/wco.0000000000001130 · Cited by 19 · see citing works
Sideris, K., Weir, C. R., Schmalfuss, C. M., Hanson, H., Pipke, M., Tseng, P.-H., Lewis, N. P., Sallam, K., Bozkurt, B., Hanff, T. C., et al. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae017 · Cited by 20 · see citing works
Stroud, A. M., Anzabi, M. D., Wise, J. L., Barry, B. A., Malik, M. M., McGowan, M. L., & Sharp, R. R. (2024) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2024.100189 · Cited by 19 · see citing works
Cooper, J. M., Haroon, S. M., Crowe, F. L., Nirantharakumar, K., Jackson, T., Fitzsimmons, L., Hathaway, E., Flanagan, S., Marshall, T. P., Jackson, L., et al. (2025) · Journal of Medical Internet Research · doi:10.2196/71980 · Cited by 17 · see citing works
Frost, E. K., Bosward, R., Aquino, Y. S. J., Braunack‐Mayer, A., & Carter, S. M. (2024) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2024.105417 · Cited by 16 · see citing works
Self-citationKowalewska, E. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1556921 · Cited by 18 · see citing works
Challenges for implementing generative artificial intelligence ( GenAI ) into clinical healthcare
Roberts, L. J., Jayasena, R., Khanna, S., Arnott, L., Lane, P., & Bain, C. (2025) · Internal Medicine Journal · doi:10.1111/imj.70035 · Cited by 15 · see citing works
Hua, J. H. (2023) · International Journal of Educational Management and Development Studies · doi:10.53378/353030 · Cited by 26 · see citing works
Liaw, W. R., Ramos Silva, Y., Soltero, E. G., Krist, A., & Stotts, A. L. (2023) · JMIR AI · doi:10.2196/45032 · Cited by 14 · see citing works
“Similar to others, they also regarded the technology with skepticism [35,41,42].”
ResultBajramagic, M., Battelino, T., Cos, X., Coté, M., Cui, N., Forbes, A., Galderisi, A., Heinemann, L., Hussain, S., Imbert, J., et al. (2025) · Diabetologia · doi:10.1007/s00125-025-06601-5 · Cited by 17 · see citing works
Gunathilaka, N. J., Gooden, T. E., Cooper, J. M., Flanagan, S., Marshall, T. P., Haroon, S. M., d’Elia, A., Crowe, F. L., Jackson, T., Nirantharakumar, K., et al. (2024) · BMJ Open · doi:10.1136/bmjopen-2023-077156 · Cited by 14 · see citing works
Exploring Patient Participation in AI-Supported Health Care: Qualitative Study
Ossa, L. A., Rost, M., Bont, N., Lorenzini, G., Shaw, D., & Elger, B. S. (2025) · JMIR AI · doi:10.2196/50781 · Cited by 13 · see citing works
Martiniussen, M. A., Larsen, M., Larsen, A. S. F., Hovda, T., Koch, H. W., Bjørnerud, A., & Hofvind, S. S. H. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.111061 · Cited by 13 · see citing works
Alsharif, W. M., Qurashi, A. A., Toonsi, F. A., Alanazi, A. H., Alhazmi, F. H., Abdulaal, O. M., Aldahery, S. T., & Alshamrani, K. M. (2022) · BJR|Open · doi:10.1259/bjro.20210029 · Cited by 13 · see citing works
Do Employees’ Artificial Intelligence Attitudes Affect Individual Business Performance?
Taşgıt, Y. E., Baykal, Y., AYDIN, U. C., YAKUPOĞLU, A., & Coşkuner, M. (2023) · Journal of Organisational Studies and Innovation · doi:10.51659/josi.22.176 · Cited by 12 · see citing works
Saßmannshausen, S. M., Ontika, N. N., de Carvalho, A. F. P., Rouncefield, M., & Pipek, V. (2024) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3613904.3642362 · Cited by 13 · see citing works
Alomran, A. K., Alomar, M. F., Akhdher, A. A., Qanber, A. R. A., Albik, A. K., Alumran, A. K., & Abdulwahab, A. H. (2024) · World Journal of Orthopedics · doi:10.5312/wjo.v15.i11.1023 · Cited by 12 · see citing works
General practitioners’ opinions of generative artificial intelligence in the UK: An online survey
Kharko, A., Sanchez, C. G., Hagström, J., Gaab, J., Locher, C., McMillan, B., Sundemo, D. S., & Blease, C. (2025) · Digital Health · doi:10.1177/20552076251360863 · Cited by 15 · see citing works
Nurses’ Intention to Integrate AI Into Their Practice: Survey Study in Canada
Paré, G., Raymond, L., & Sosso, F. A. E. (2025) · JMIR Nursing · doi:10.2196/76795 · Cited by 16 · see citing works
Waldock, W. J., Guni, A., Darzi, A. W., & Ashrafian, H. (2026) · PLOS Digital Health · doi:10.1371/journal.pdig.0001310 · Cited by 14 · see citing works
Maitz, K., Fessl, A., Pammer‐Schindler, V., Kaiser, R., & Lindstaedt, S. (2022) · Mensch und Computer (MuC) · doi:10.1145/3543758.3547545 · Cited by 11 · see citing works
Environment scan of generative AI infrastructure for clinical and translational science
Idnay, B., Xu, Z., Adams, W. G., Adibuzzaman, M., Anderson, N. R., Bahroos, N., Bell, D. S., Bumgardner, C., Campion, T. R., Castro, M., et al. (2025) · npj Health Systems · doi:10.1038/s44401-024-00009-w · Cited by 16 · see citing works
Lauderdale, S. A., Schmitt, R., Wuckovich, B., Dalal, N., Desai, H., & Tomlinson, S. (2025) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2025.1544951 · Cited by 11 · see citing works
Iskrov, G. G., Raycheva, R., Kostadinov, K. R., Gillner, S., Blankart, C. R., Gross, E. S., Gumus, G., Митова, Е., Stefanov, S. A., Стефанов, Г., et al. (2024) · Orphanet Journal of Rare Diseases · doi:10.1186/s13023-024-03047-7 · Cited by 9 · see citing works
Roberts, F., Roberts, T., Gelfer, Y., & Hing, C. B. (2024) · Annals of The Royal College of Surgeons of England · doi:10.1308/rcsann.2024.0008 · Cited by 9 · see citing works
Patient perceptions of artificial intelligence in dental imaging diagnostics: a multicentre survey
Tirapelli, C., Gaêta‐Araujo, H., Costa, E. D., Scarfe, W. C., Oliveira‐Santos, C., Fischer, K. M., Grosgogeat, B., Szönyi, V., Melo, P., Marrara, J. L., et al. (2025) · Dentomaxillofacial Radiology · doi:10.1093/dmfr/twaf018 · Cited by 9 · see citing works
Hendrix, N., Maisel, N. C., Everson, J., Patel, V., Bazemore, A., Rotenstein, L. S., Holmgren, A. J., Krist, A. H., Adler‐Milstein, J., & Phillips, R. L. (2024) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocae148 · Cited by 9 · see citing works
Riley, R. F., Alasnag, M. A., Batchelor, W., Sharma, A., Luse, E., Drewes, M., Welt, F. G. P., Itchhaporia, D., & Henry, T. D. (2022) · Journal of the Society for Cardiovascular Angiography & Interventions · doi:10.1016/j.jscai.2022.100307 · Cited by 9 · see citing works
Being diagnosed with a rhabdomyosarcoma in the era of artificial intelligence: whom can we trust?
Clerici, C. A., Bernasconi, A., Lasalvia, P., Bisogno, G., Milano, G. M., Trama, A., Chiaravalli, S., Bergamaschi, L., Casanova, M., Massimino, M., et al. (2024) · Pediatric Blood & Cancer · doi:10.1002/pbc.31256 · Cited by 8 · see citing works
Scott, I. A., Shaw, T. J., Slade, C., Wan, T. T., Barmanray, R., Coorey, C. P., Johnson, S. L., Bell, L. M., Herd, M., & Sullivan, C. M. (2025) · Internal Medicine Journal · doi:10.1111/imj.70112 · Cited by 8 · see citing works
Self-citationNichol, B., Hurlbert, A. C., & Read, J. C. A. (2022) · Journal of Public Health Research · doi:10.1177/22799036221127627 · Cited by 8 · see citing works
Gedik, N., Işıkoğlu, M. A., & Şendağ, S. (2025) · Interactive Learning Environments · doi:10.1080/10494820.2025.2523379 · Cited by 7 · see citing works
Lammons, W., Moss, B., Bignell, C., Gale, C., MacBride, A., Ribas, R., Battersby, C., & Modi, N. (2023) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2022-100694 · Cited by 7 · see citing works
New Advances in Artificial Intelligence for Biomedical Research and Clinical Decision-MakingPreprint
Iftimia, N. A., Pandya, R., & Mahoney, F. (2023) · Preprints.org · doi:10.20944/preprints202306.0243.v1 · Cited by 6 · see citing works
Artificial Intelligence in Gynaecology Oncology
Sawan, S., Eftekhari, N., Linton‐Reid, K., Wood, N., Numan, T. A., Aboagye, E. O., & Angione, C. (2025) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.70005 · Cited by 13 · see citing works
Raymond, L., Paré, G., Doyon, O., & Wagner, G. (2025) · Journal of Advanced Nursing · doi:10.1111/jan.70307 · Cited by 6 · see citing works
Arrooqi, A. S., & Alruqi, M. M. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05598-x · Cited by 7 · see citing works
Pokorny, M. A., Liang, V., Thorne, P. R., MacFater, W., Habib, A., Mahadevan, M., & Holt, E. (2025) · BMC Digital Health · doi:10.1186/s44247-025-00202-3 · Cited by 5 · see citing works
Zhang, M., Chu, R., Liu, C., Zhang, S., & Ren, X. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1684035 · Cited by 7 · see citing works
Reeder, S. C., Howard, M., Sparrow, R., Foster, E., Ge, Z., & Kwan, P. (2025) · Epilepsy & Behavior · doi:10.1016/j.yebeh.2025.110720 · Cited by 5 · see citing works
Interpretable machine learning-based detection of coeliac disease
Jaeckle, F., Bryant, R., Denholm, J., Diaz, J. R., Schreiber, B., Shenoy, V., Ekundayomi, D., Evans, S. C., Arends, M. J., & Soilleux, E. J. (2025) · BMJ Digital Health & AI · doi:10.1136/bmjdhai-2025-000023 · Cited by 6 · see citing works
Adoption of AI-Based Technologies in the Food Supplement Industry: An Italian Start-Up Case Study
Rapa, M., Ciano, S., Orsini, F., Tullo, M. G., Giannetti, V., & Mariani, M. B. (2023) · Systems · doi:10.3390/systems11060265 · Cited by 5 · see citing works
Patient perceptions on the use of artificial intelligence (AI) in fertility treatment
Fee, N., Glover, L. E., Bauman, R., & Crosby, D. A. (2025) · Human Fertility · doi:10.1080/14647273.2025.2591161 · Cited by 4 · see citing works
- Authors
- Scott, I. A., Carter, S. M., & Coiera, E.
- Affiliations
- The University of Queensland (AU)
- University of Wollongong (AU)
- Macquarie University (AU)
- Corresponding author
- Scott, Ian A
- Venue
- BMJ Health & Care Informatics
- Publisher
- BMJ
- Published
- 2021-12
- Volume
- 28
- Issue
- 1
- Pages
- e100450
- ISSN
- 2632-1009 (ISSN-L)
- PMC
- PMC8663096
- OpenAlex
- W4200394808
- Cited by
- 193
- References
- 40
- Citations by year
- 2026: 45 · 2025: 61 · 2024: 34 · 2023: 39 · 2022: 14
- FWCI
- 1.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMJ Health & Care Informatics (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence in healthcare, stakeholder attitudes, clinician attitudes, consumer attitudes, AI acceptance, clinical decision support, privacy concerns, liability for AI errors, clinician oversight, regulatory certainty, clinician-patient relationship
- MeSH terms
- Artificial Intelligence, Attitude, Delivery of Health Care, Humans, Names
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
