Evaluation of artificial intelligence clinical applications: Detailed case analyses show value of healthcare ethics approach in identifying patient care issues
Open accessAbstract
This paper is one of the first to analyse the ethical implications of specific healthcare artificial intelligence (AI) applications, and the first to provide a detailed analysis of AI‐based systems for clinical decision support. AI is increasingly being deployed across multiple domains. In response, a plethora of ethical guidelines and principles for general AI use have been published, with some convergence about which ethical concepts are relevant to this new technology. However, few of these frameworks are healthcare‐specific, and there has been limited examination of actual AI applications in healthcare. Our ethical evaluation identifies context‐ and case‐specific healthcare ethical issues for two applications, and investigates the extent to which the general ethical principles for AI‐assisted healthcare expressed in existing frameworks capture what is most ethically relevant from the perspective of healthcare ethics. We provide a detailed description and analysis of two AI‐based systems for clinical decision support (Painchek® and IDx‐DR). Our results identify ethical challenges associated with potentially deceptive promissory claims, lack of patient and public involvement in healthcare AI development and deployment, and lack of attention to the impact of AIs on healthcare relationships. Our analysis also highlights the close connection between evaluation and technical development and reporting. Critical appraisal frameworks for healthcare AIs should include explicit ethical evaluation with benchmarks. However, each application will require scrutiny across the AI life‐cycle to identify ethical issues specific to healthcare. This level of analysis requires more attention to detail than is suggested by current ethical guidance or frameworks.
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Rogers et al shared a very practical approach to how they evaluated an AI model for ethics.
“Similarly, Rogers et al [97] shared a very practical approach to how they evaluated an AI model for ethics.”
From Barriers to and facilitators of artificial intelligence adoption in health care: scoping review (2024) · cites this paper as [97]
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- 17 Abràmoff, M.D., Lavin, P.T., Birch, M., Shah, N., & Folk, J.C. (2018). Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. npj Digital Medicine . 1 (1), 39; Van Der Heijden, A.A., Abràmoff, M.D., Verbraak, F., Hecke, M.V., Liem, A., & Nijpels, G. (2018). Validation of automated screening for referable diabetic retinopathy with the IDx-DR device in the Hoorn Diabetes Care System. Acta Ophthalmologica . 96 (1), 63-68; Verbraak, F.D., Abràmoff, M.D., Bausch, G.C.F., Klaver, C., Nijpels, G., Schlingemann, ReinierO., & van der Heijden, A.A. (2019). Diagnostic Accuracy of a Device for the Automated Detection of Diabetic Retinopathy in a Primary Care Setting. Diabetes Care . 42 (4), 651-656.
- For Peer Review 15 Painchek ® , op cit . note 12. 16 Anon. (2019, December 1). The Non-Executive Chairman of PainChek Ltd. (ASX:PCK), John Murray, Just Sold 50% Of Their Holding. Simply Wall St . Retrieved from https://simplywall.st/stocks/au/healthcare/asx-pck/painchek-shares/news/the-nonexecutive-chairman-of-painchek-ltd-asxpck-john-murray-just-sold-50-of-their-holding/ Accessed 8 Feb 2021]. until they could be resolved. This produced the final list of issues for each use-case, presented in chronological order across the AI lifecycle (in Section 3).
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Schlicht, L., & Räker, M. (2023) · AI and Ethics · doi:10.1007/s43681-023-00324-2 · Cited by 18 · see citing works
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“Equity and social justice must be at the forefront to ensure these technologies benefit all patients, not just select groups [116,117].”
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Wojda, T. R., Hoffman, C., Kindler, K., Desai, A., & Visweswaran, S. (2024) · Artificial intelligence · doi:10.5772/intechopen.114907 · Cited by 2 · see citing works
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Forlini, C. (2023) · doi:10.1002/9781394257522.ch1 · Cited by 2 · see citing works
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Behdadi, D., Vanfleteren, L. E. G. W., & Sundemo, D. S. (2025) · AI & Society · doi:10.1007/s00146-025-02806-9 · Cited by 1 · see citing works
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Ho, A. (2023) · Oxford University Press eBooks · doi:10.1093/med/9780197556269.003.0001 · Cited by 1 · see citing works
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Kim, J. (2024) · Korean Journal of Medical Ethics · doi:10.35301/ksme.2024.27.4.177 · Cited by 1 · see citing works
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Srivastava, A., Marabelli, M., Blanch‐Hartigan, D., Moriarty, J., Carey, E., Persky, S., & Torous, J. B. (2025) · Communications of the Association for Information Systems · doi:10.17705/1cais.05611 · Cited by 1 · see citing works
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Aralova, N. I., Чикрий, А. А., & Шахліна, Л. (2024) · Kibernetyka ta Systemnyi Analiz · doi:10.34229/kca2522-9664.24.6.15
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Aralova, N. I., Chikrii, A. A., & Шахліна, Л. (2024) · Cybernetics and Systems Analysis · doi:10.1007/s10559-024-00736-w
de Menezes, F. R., Silva, E., Alcântara, F. A., et al. (2025) · Revista Contemporânea · doi:10.56083/rcv5n8-071
Dascălu, C. G., Forna, N. C., Georgescu, A., & Topoliceanu, C. (2025) · Romanian Journal of Oral Rehabilitation · doi:10.62610/rjor.2025.1.17.82
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Tran, T. T. T., & Wang, X. (2025) · ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) · doi:10.1109/snpd65828.2025.11254312 · Cited by 2 · see citing works
“In addition, due to a small number of trial participants, the absence of blinding, and the lack of independent operators, PainChek®’s accuracy remains uncertain [16].”
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“Normative ethics [125-127 Minority groups may be discriminated against by an algorithm that learns to normalize or prioritize patterns evident in the majority.”
- Authors
- Rogers, W. A., Draper, H., & Carter, S. M.
- Affiliations
- Macquarie University (AU)
- University of Warwick (GB)
- University of Wollongong (AU)
- Corresponding author
- Rogers, Wendy A.
- Venue
- Bioethics
- Publisher
- Wiley
- Published
- 2021-05-28
- Volume
- 35
- Issue
- 7
- Pages
- 623-633
- ISSN
- 0269-9702 (ISSN-L)
- 1467-8519
- OpenAlex
- W3164685439
- Cited by
- 76
- References
- 0
- Citations by year
- 2026: 7 · 2025: 17 · 2024: 24 · 2023: 23 · 2022: 1 · 2021: 4
- FWCI
- 3.76
- Type
- journal article
- Language
- en
- Update notices
- correction (2022-04-20)
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioethics (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Research Online (University of Wollongong) (submittedVersion)
- Figshare (submittedVersion)
- Funders
- National Health and Medical Research Council (1181960)
- Canadian Institute for Advanced Research
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Ethics and bioethics in healthcare
- Keywords
- artificial intelligence in healthcare, clinical decision support, health care ethics, patient and public involvement, AI lifecycle
- MeSH terms
- Artificial Intelligence, Bioethics, Delivery of Health Care, Patient Care, Health Facilities, Humans
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