The need for health AI ethics in medical school education
Abstract
Health Artificial Intelligence (AI) has the potential to improve health care, but at the same time, raises many ethical challenges. Within the field of health AI ethics, the solutions to the questions posed by ethical issues such as informed consent, bias, safety, transparency, patient privacy, and allocation are complex and difficult to navigate. The increasing amount of data, market forces, and changing landscape of health care suggest that medical students may be faced with a workplace in which understanding how to safely and effectively interact with health AIs will be essential. Here we argue that there is a need to teach health AI ethics in medical schools. Real events in health AI already pose ethical challenges to the medical community. We discuss key ethical issues requiring medical school education and suggest that case studies based on recent real-life examples are useful tools to teach the ethical issues raised by health AIs.
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Several ethical concerns regarding the use of AI in UGME are described, including algorithmic bias and safeguarding the privacy of students and patients.
“Several ethical concerns regarding the use of AI in UGME are described [183]. There are concerns related to algorithmic bias [159] and safeguarding the privacy of students and patients [185, 186].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [186]
Fifteen publications focused explicitly on ethical considerations concerning the use of AI in medical education.
“15 publications focused explicitly on this topic [7, 38, 84, 97, 186, 190–199].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [186]
Numerous articles (5%) provide guidelines and "tips" on AI in medical education.
“Numerous articles (5%) provide guidelines and “tips” on AI in medical education [57, 83, 94, 135, 136, 162, 179, 186, 195, 196, 202, 205–210].”
From Artificial intelligence in undergraduate medical education: an updated scoping review (2025) · cites this paper as [186]
Medical chatbots provide diagnostic and treatment recommendations in order to reduce unnecessary doctor visits. However, these medical chatbots may also harm patients if they are not continuously updated, checked, or regulated.
“medical chatbots provide diagnostic and treatment recommendations in order to reduce unnecessary doctor visits. However, these medical chatbots may also harm patients if they are not continuously updated, checked, or regulated”
From Ethics and governance of trustworthy medical artificial intelligence (2023) · cites this paper as [7]
Recent publications emphasize the importance of teaching AI ethics in medical education.
“In addition to the technical and legal aspects of the use of AI in medicine, recent publications emphasize the importance of teaching AI ethics in medical education [9-11].”
From Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education (2024) · cites this paper as [9]
One identified publication defined 6 potential topics for AI ethics teaching: informed consent, bias, safety, transparency, patient privacy, and allocation.
“In one of the 2 identified publications specifically addressing AI ethics teaching content for medical education, 6 potential topics were defined: informed consent, bias, safety, transparency, patient privacy, and allocation [9].”
From Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education (2024) · cites this paper as [9]
The development of AI-based applications involves extensive data sets, raising concerns regarding data privacy and the potential for expropriation of personal health data.
“Moreover, the development of AI-based applications involves extensive data sets, raising concerns regarding data privacy and the potential for expropriation of personal health data [9].”
From Proposing a Principle-Based Approach for Teaching AI Ethics in Medical Education (2024) · cites this paper as [9]
Katznelson et al. present six specific ethical challenges ('informed consent', 'bias', 'safety', 'transparency', 'patient privacy', and 'allocation') that should be addressed as part of medical school teaching and student training.
“Katznelson et al. not only illustrate the relevance of teaching ethics on AI but also present six specific ethical challenges (‘informed consent’, ‘bias’, ‘safety’, ‘transparency’, ‘patient privacy’, and ‘allocation’) that should be addressed as part of medical school teaching and student training [47].”
From Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices (2023) · cites this paper as [47]
Katznelson et al. (2021) was the first publication to focus on the definition of AI ethics and integration of AI in medical education and to formulate specific recommendations for teaching content by anticipating ethical challenges from AI in medicine.
“The publication by Katznelson et al. (2021) was identified as the first to not only focus on the definition of AI ethics and the integration of AI in medical education but also to formulate specific recommendations for teaching content by anticipating ethical challenges and problems that may arise from the use of AI in medicine [47].”
From Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices (2023) · cites this paper as [47]
Out of 12 publications included, only one provided a definition of AI ethics.
“Out of 12 publications included in this review, only one provided a definition of AI ethics [47].”
From Teaching AI Ethics in Medical Education: A Scoping Review of Current Literature and Practices (2023) · cites this paper as [47]
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- Authors
- Katznelson, G., & Gerke, S.
- Affiliations
- Western University (CA)
- Harvard University (US)
- Corresponding author
- Gerke, Sara
- ORCID
- Gerke, Sara
- Venue
- Advances in Health Sciences Education
- Published
- 2021-03-03
- Volume
- 26
- Issue
- 4
- Pages
- 1447-1458
- ISSN
- 1382-4996 (ISSN-L)
- 1573-1677
- OpenAlex
- W3135353559
- Cited by
- 125
- References
- 8
- Citations by year
- 2026: 24 · 2025: 37 · 2024: 28 · 2023: 27 · 2022: 7 · 2021: 2
- FWCI
- 4.63
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Advances in Health Sciences Education (publishedVersion)
- PubMed (publishedVersion)
- eYLS (Yale Law School) (submittedVersion)
- bepress Legal Repository (submittedVersion)
- Funders
- Novo Nordisk Fonden (NNF17SA0027784)
- 2019 Ontario Medical Student Association (OMSA) Medical Student Education Research Grant
- 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
- AI ethics, medical education, medical students, informed consent, algorithmic bias, patient privacy, transparency, safety, resource allocation, case-based teaching
- MeSH terms
- Artificial Intelligence, Delivery of Health Care, Education, Medical, Ethics, Medical, Humans, Schools, Medical, Students, Medical
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