The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
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.
Overview
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
Verification
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?
The cited source says what the claim attributes to it.
In the citing paper
“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.
Citations
Cites
Eysenbach, G. (2004) · Journal of Medical Internet Research · doi:10.2196/jmir.6.3.e34 · Cited by 6,480
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579
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
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
Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338
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
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
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
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
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
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
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
Gerke, S., Babic, B., Evgeniou, T., & Cohen, I. G. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0262-2 · Cited by 359
Fan, W., Liu, J., Zhu, S., & Pardalos, P. M. (2020) · Annals of Operations Research · doi:10.1007/s10479-018-2818-y · Cited by 346
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
My Friends, Editors, Algorithms, and I
Thurman, N., Möller, J. E., Helberger, N., & Trilling, D. (2018) · Digital Journalism · doi:10.1080/21670811.2018.1493936 · Cited by 332
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
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
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
Laï, M.-C., Brian, M., & Mamzer, M.-F. (2020) · Journal of Translational Medicine · doi:10.1186/s12967-019-02204-y · Cited by 294
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 292
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
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 282
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
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
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
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
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 177
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
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 141
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 121
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
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
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
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 91
Sisk, B. A., Antes, A. L., Burrous, S., & DuBois, J. M. (2020) · Children · doi:10.3390/children7090145 · Cited by 77
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
Clinical Laboratory Employees’ Attitudes Toward Artificial Intelligence
Ardon, O., & Schmidt, R. L. (2020) · Laboratory Medicine · doi:10.1093/labmed/lmaa023 · Cited by 36
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
Perlmutter, A. S., Benchoufi, M., Ravaud, P., & Tran, V.-T. (2020) · Journal of Medical Internet Research · doi:10.2196/18986 · Cited by 14
Research works printed but not linked: 1
- 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
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
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 272
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
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 218
Metadata
- 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
- Physical Sciences › Computer Science › Artificial Intelligence › 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
