Ethical dimensions of using artificial intelligence in health care
Open accessAbstract
An artificially intelligent computer program can now diagnose skin cancer more accurately than a board-certified dermatologist. 1 Better yet, the program can do it faster and more efficiently, requiring a training data set rather than a decade of expensive and labor-intensive medical education.While it might appear that it is only a matter of time before physicians are rendered obsolete by this type of technology, a closer look at the role this technology can play in the delivery of health care is warranted to appreciate its current strengths, limitations, and ethical complexities.Artificial intelligence (AI), which includes the fields of machine learning, natural language processing, and robotics, can be applied to almost any field in medicine, 2 and its potential contributions to biomedical research, medical education, and delivery of health care seem limitless.With its robust ability to integrate and learn from large sets of clinical data, AI can serve roles in diagnosis, 3 clinical decision making, 4 and personalized medicine.5 For example, AI-based diagnostic algorithms applied to mammograms are assisting in the detection of breast cancer, serving as a "second opinion" for radiologists.6 In addition, advanced virtual human avatars are capable of engaging in meaningful conversations, which has implications for the diagnosis and treatment of psychiatric disease.7 AI applications also extend into the physical realm with robotic prostheses, physical task support systems, and mobile manipulators assisting in the delivery of telemedicine.8
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Ongoing work on federated learning and secure multi-party computation might inform how agents can operate on sensitive data without pooling it centrally.
“Ongoing work on federated learning and secure multi-party computation might inform how agents can operate on sensitive data without pooling it centrally [60].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [60]
The editor’s discussion of privacy and confidentiality in AI health care (Abstract; page not provided) does not address federated learning, secure multi-party computation, or operating on sensitive data without central pooling. Searches for those techniques and for privacy-preserving distributed learning found no passage in our copy that speaks to the claim. (LLM, conf=0.74)
Experimental — accuracy not yet measuredStudies on physician attitudes toward AI have found that clear explanations and the ability to veto or override AI suggestions are important for acceptance.
“Studies on physician attitudes toward AI have found that clear explanations and the ability to veto or override AI suggestions are important for acceptance [59,60].”
From A Review of Multi-Agent AI Systems for Biological and Clinical Data Analysis (2026) · cites this paper as [60]
The editor’s article (page not provided) discusses physician input, AI as a complementary tool, and difficulty explaining black-box outputs. It does not report studies of physician attitudes or say that clear explanations and the ability to override AI suggestions affect acceptance. Searches for physician acceptance, explanations, vetoes, and overrides found no supporting passage. (LLM, conf=0.76)
Experimental — accuracy not yet measuredThere remains an incomplete awareness within the medical community of how emerging AI technology can introduce ethical complexities into actual care taking.
“There remains an incomplete awareness within the medical community of how emerging AI technology can introduce ethical complexities into actual care taking”
From SHIFTing artificial intelligence to be responsible in healthcare: a systematic review (2022) · cites this paper as Rigby, 2019
Common ethical concerns about the use of AI in medicine include lack of transparency, insufficient knowledge about the application used, or false/misleading results.
“Common ethical concerns about the use of AI in medicine include lack of transparency, insufficient knowledge about the application used, or false/misleading results [15, 16].”
From Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education (2023) · cites this paper as [15]
There is a risk that AI systems will compromise patient autonomy and dignity by making treatment decisions without appropriate oversight.
“Finally, there is a risk that AI systems will compromise patient autonomy and dignity by making treatment decisions without appropriate oversight [15, 20].”
From Biomedical Ethical Aspects Towards the Implementation of Artificial Intelligence in Medical Education (2023) · cites this paper as [15]
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References 12 · Cited by 328
Research works printed but not linked: 1
- Michael J. Rigby is a fifth-year student in the Medical Scientist Training Program (MSTP) at the University of Wisconsin School of Medicine and Public Health in Madison. He is currently a PhD candidate in molecular neuroscience and is studying the mechanisms that underlie neurodegenerative diseases. He earned a BS in molecular and cellular biology at the University of Illinois at Urbana-Champaign and is interested in pursuing a career as a physician-scientist in neurology. Citation AMA J Ethics. 2019;21(2):E121-124. DOI 10.1001/amajethics.2019.121.
- Authors
- Rigby, M. J.
- Corresponding author
- Michael J. Rigby
- ORCID
- Michael J. Rigby
- Venue
- AMA Journal of Ethics
- Publisher
- American Medical Association (AMA)
- Published
- 2019-02-01
- Volume
- 21
- Issue
- 2
- Pages
- E121-124
- ISSN
- 1937-7010 (ISSN-L)
- 2376-6980
- OpenAlex
- W2914383481
- Cited by
- 328
- References
- 12
- Citations by year
- 2026: 19 · 2025: 62 · 2024: 41 · 2023: 95 · 2022: 34 · 2021: 43 · 2020: 28 · 2019: 6
- FWCI
- 8.97
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- The AMA Journal of Ethic (publishedVersion)
- 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, medical ethics, clinical decision-making, AI-assisted diagnosis, machine learning, natural language processing, robotic prostheses, telemedicine, personalized medicine, breast cancer detection, skin cancer diagnosis
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