Artificial intelligence in healthcare: a critical analysis of the legal and ethical implications
Abstract
Artificial intelligence (AI) is perceived as the most transformative technology of the 21st century. Healthcare has been identified as an early candidate to be revolutionized by AI technologies. Various clinical and patient-facing applications have already reached healthcare practice with the potential to ease the pressure on healthcare staff, bring down costs and ultimately improve the lives of patients. However, various concerns have been raised as regards the unique properties and risks inherent to AI technologies. This article aims at providing an early stage contribution with a holistic view on the ‘decision-making’ capacities of AI technologies. The possible ethical and legal ramifications will be discussed against the backdrop of the existing frameworks. I will conclude that the present structures are largely fit to deal with the challenges AI technologies are posing. In some areas, sector-specific revisions of the law may be advisable, particularly concerning non-discrimination and product liability.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
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?
This finding may partly resonate with the argument that explaining the 'inner workings' of the AI algorithm does not necessarily empower patients to make more informed choices regarding their treatment options.
“If we assume that participants with higher education have a better understanding of which information is actually crucial in deciding whether to accept a treatment, this finding may partly resonate with the argument that explaining the ‘inner workings’ of the AI algorithm does not necessarily empower patients to make more informed choices regarding their treatment options. 16”
From Patient perspectives on informed consent for medical AI: A web-based experiment (2024) · cites this paper as [16]
Cited by
Sorted by citations
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective
the Precise4Q consortium, Amann, J., Blasimme, A., Vayena, E., Frey, D., & Madai, V. I. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01332-6 · Cited by 2,203 · see citing works
The ethics of AI in health care: A mapping review
Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020) · Social Science & Medicine · doi:10.1016/j.socscimed.2020.113172 · Cited by 1,105 · see citing works
Artificial Intelligence and Human Trust in Healthcare: Focus on Clinicians
Asan, O., Bayrak, A. E., & Choudhury, A. (2020) · Journal of Medical Internet Research · doi:10.2196/15154 · Cited by 825 · see citing works
Drawbacks of artificial intelligence and their potential solutions in the healthcare sector.
khan, B., Fatima, H., Qureshi, A., Kumar, S., Hanan, A., Hussain, J., & Abdullah, S. (2023) · Biomedical Materials & Devices · doi:10.1007/s44174-023-00063-2 · Cited by 461 · see citing works
Artificial Intelligence and Patient-Centered Decision-Making
Bjerring, J. C., & Busch, J. (2021) · Philosophy & Technology · doi:10.1007/s13347-019-00391-6 · Cited by 274 · see citing works
Recht, M. P., Dewey, M., Dreyer, K., Langlotz, C., Niessen, W., Prainsack, B., & Smith, J. J. (2020) · European Radiology · doi:10.1007/s00330-020-06672-5 · Cited by 268 · see citing works
Responsible AI for Digital Health: a Synthesis and a Research Agenda
Trocin, C., Mikalef, P., Papamitsiou, Z., & Conboy, K. (2023) · Information Systems Frontiers · doi:10.1007/s10796-021-10146-4 · Cited by 205 · see citing works
An AI-based decision support system for predicting mental health disorders
Tutun, S., Johnson, M. E., Ahmed, A., Albizri, A., Irgil, S., Yesilkaya, I., Ucar, E. N., Sengun, T., & Harfouche, A. (2023) · Information Systems Frontiers · doi:10.1007/s10796-022-10282-5 · Cited by 177 · see citing works
Prakash, S., Balaji, J. N., Joshi, A., & Surapaneni, K. M. (2022) · Journal of Personalized Medicine · doi:10.3390/jpm12111914 · Cited by 159 · see citing works
Artificial Intelligence in Radiology—Ethical Considerations
Brady, A. P., & Neri, E. (2020) · Diagnostics · doi:10.3390/diagnostics10040231 · Cited by 150 · see citing works
The integration of AI in nursing: addressing current applications, challenges, and future directions
Wei, Q., Pan, S., Liu, X., Hong, M., Nong, C., & Zhang, W. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1545420 · Cited by 143 · see citing works
Patient perspectives on informed consent for medical AI: A web-based experiment
Park, H. J. (2024) · DIGITAL HEALTH · doi:10.1177/20552076241247938 · Cited by 95 · see citing works
Bernal, J., & Mazo, C. (2022) · Applied Sciences · doi:10.3390/app122010228 · Cited by 70 · see citing works
The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review
Morley, J., Machado, C., Burr, C., Cowls, J., Taddeo, M., & Floridi, L. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3486518 · Cited by 66 · see citing works
Holm, S., Stanton, C., & Bartlett, B. (2021) · Health Care Analysis · doi:10.1007/s10728-021-00430-4 · Cited by 32 · see citing works
In support of “no-fault” civil liability rules for artificial intelligence
Marchisio, E. (2021) · SN Social Sciences · doi:10.1007/s43545-020-00043-z · Cited by 25 · see citing works
“27 See, on this point, the “Swiss cheese model” developed in Reason (1990).”
BackgroundEvaluation of knowledge and attitudes of radiology department workers about artificial intelligence
Yurdaisik, I., & Aksoy, S. H. (2021) · Annals of Clinical and Analytical Medicine · doi:10.4328/acam.20453 · Cited by 13 · see citing works
“It is unclear who will be responsible for poor outcomes of AI in the future [11].”
BackgroundBarriers to Artificial Intelligence Adoption in Healthcare Management: A Systematic Review
Assadullah, M. M. (2019) · SSRN Electronic Journal · doi:10.2139/ssrn.3530598 · Cited by 13 · see citing works
Donnelly, D.-L. (2022) · Potchefstroom Electronic Law Journal · doi:10.17159/1727-3781/2022/v25i0a11118 · Cited by 8 · see citing works
- Authors
- Schönberger, D.
- Affiliations
- University of Edinburgh (GB)
- Corresponding author
- Schönberger, Daniel
- ORCID
- Schönberger, Daniel
- Venue
- International Journal of Law and Information Technology
- Publisher
- Oxford University Press (OUP)
- Published
- 2019
- Volume
- 27
- Issue
- 2
- Pages
- 171-203
- ISSN
- 0967-0769 (ISSN-L)
- 1464-3693
- OpenAlex
- W2944309093
- Cited by
- 230
- References
- 0
- Citations by year
- 2026: 22 · 2025: 37 · 2024: 52 · 2023: 31 · 2022: 31 · 2021: 30 · 2020: 21 · 2019: 5
- FWCI
- 189.82
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model (vor)
- Locations
- International Journal of Law and Information Technology (publishedVersion)
- Topics
- Social Sciences › Law › Digitalization, Law, and Regulation
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- artificial intelligence in healthcare, legal implications of AI, ethical implications of AI, AI decision-making, legal frameworks for AI, non-discrimination, product liability, health law
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
