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Medical Artificial Intelligence and Human Values
Open accessAbstract
Recent progress in generative artificial intelligence (AI) has given rise to large language models (LLMs) that can be prompted to craft persuasive essays, 1 pass professional competency exams, [2][3][4] and write patient-friendly empathetic messages.5 Amid growing recognition of the capabilities of LLMs, many have expressed concerns about their use in medicine and healthcare, citing known risks of confabulation, fragility, and factual inaccuracy.6 As these risks are measured and mitigated, what is coming into focus are unresolved questions about the human values that will remain embedded in AI, both in their creation and in their use, and how the "values of an LLM" may misalign with human values even if AI models no longer confabulate and have been scrubbed of obvious toxic output.Such "human values" pertain broadly to the principles, standards, and preferences that reflect human goals and guide human behaviors (Glossary).As we review here, LLMs and new foundation models, as technically impressive as they are, are only the latest incarnation of a long line of probabilistic models integrated into medical decision-making, all of which have required their creators and implementers to make value judgments.Many of the challenges we address here were evident to the pioneers of medical decision analysis of the 1950s 7 and to scholars in subsequent decades [8][9][10][11] who conducted careful and creative studies of both human and algorithmic decision-making to disentangle probability, the chance of an event occurring, from utilities, the quantified value judgments that are often only indirectly articulated in much of medical decision-making (Glossary).The nuanced understanding of individual values and risks is what makes the thoughtful clinician so indispensable.These considerations have renewed relevance with unprecedented and ubiquitously available AI models like LLMs.In this article, we first describe how value judgments enter predictive models, using familiar clinical settings and new AI language models.We then connect early work in reasoning about probabilities and utilities to the emerging issues of newer AI models, identifying unresolved challenges and future opportunities in designing high-performance and safe AI models.
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“Consequently, AI development is led by major technology companies whose goals may not always align with those of patients or healthcare systems [10, 11].”
From Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22. (2025) · cites this paper as [10]
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Metadata
- Authors
- Yu, K.-H., Healey, E., Leong, T.-Y., Kohane, I. S., & Manrai, A. K.
- Affiliations
- Brigham and Women's Hospital (US)
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2024-05-30
- Volume
- 390
- Issue
- 20
- Pages
- 1895-1904
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- OpenAlex
- W4399120295
- Cited by
- 149
- References
- 70
- Citations by year
- 2026: 47 · 2025: 85 · 2024: 17
- FWCI
- 3.40
- Type
- journal article
- Language
- en
- Open access
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Heart, Lung, and Blood Institute (R01HL174679)
- National Institute of General Medical Sciences (R35GM142879)
- 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 › Explainable Artificial Intelligence (XAI)
- Keywords
- large language models, generative AI, human values, clinical decision-making, algorithmic decision-making, AI safety, foundation models, clinical decision support
- MeSH terms
- Clinical Decision-Making, Clinical Reasoning, Artificial Intelligence, Humans, Language, Risk, Social Values, Bias
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