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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AI development is led by major technology companies whose goals may not always align with those of patients or healthcare systems.
“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]
The Values Embedded in Large Language Models section describes models produced by organizations including Google, and the Motivating Clinical Example asks whether model outputs reflect patients’ values or those of other parties. This supports the concern about potentially misaligned goals, but the paper does not establish that major technology companies lead AI development. The indexed passages provide no page numbers. (LLM, conf=0.83)
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“30 Last, there is a need for increased investments in research regarding health care operations, including how to employ AI to optimize workflows, resource allocation, scheduling, supply chain, and revenue-cycle operations, among others.”
A foundational architecture for AI agents in healthcare
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The Ethics of AI in Healthcare: An Updated Mapping Review
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Economics and Equity of Large Language Models: Health Care Perspective
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“Such a model is also expected to be better utilized while demonstrating value, since the training data can significantly impact its behavior and performance [115] .”
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“…reference datasets by experts to standardize algorithm evaluation, validating AI and machine learning tools using multi-institutional data to ensure generalizability, and implementing periodic model retraining and regular output monitoring to mitigate risks and align AI with human values [6,130].”
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Large language models for clinical decision support in gastroenterology and hepatology
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“This aspect necessitates specific guardrails to ensure fairness and reproducibility 182 .”
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Neuro-symbolic AI for auditable cognitive information extraction from medical reports
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Accelerating Science with Human+AI Review
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GPT-4's performance in supporting physician decision-making in nephrology multiple-choice questions
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“To better assess these technologies in clinical settings, it is crucial to consider scenarios where physicians utilize them 16,17 .”
AI Agents, Automaticity, and Value Alignment in Health Care
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Applications of artificial intelligence‐driven microfluidics in medical laboratory science
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Scaling medical AI across clinical contexts
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“The impact of context switching depends on the model’s design, who develops it and whose context it reflects 114 .”
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Right Care, Right Place, First Time: How AI Is Improving National Virtual Front Doors
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“…research findings or changes to clinical criteria for diagnosis and disease activity emerge, DL models must be modified and revalidated to ensure continued reliability and effectiveness [148]. depending on how data is selected, weighted, and analyzed, so human decision-making remains vital [150].”
Evidence-Based Medicine: Past, Present, Future
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Evaluating the o1 reasoning large language model for cognitive bias: a vignette study
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“…as logical, objective tools-they are, upon reflection, consistent with how these models are developed: by learning from data and information generated by humans and through human-supervised learning methods, LLMs may inherit not only medical knowledge but also errors in human reasoning [16, 17].”
Medical Artificial Intelligence and Human Values
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Relational Ethics and Structural Epistemic Injustice of AI in Medicine
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“Some authors have argued for explainable AI as a prerequisite for incorporating value-based arguments into the (shared) medical decision-making process (Bjerring & Busch, 2021; Herzog, 2022b; McDougall, 2019; Yu et al., 2024).”
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Artificial intelligence‐driven change redefining radiology through interdisciplinary innovation
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“It is important that LLM outputs align with human values, which can be achieved through supervised reinforcement learning with input from expert physicians and patients [15].”
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“However, the effective integration of AI into routine oncology practice requires not only robust technologies but also clinicians who are equipped with the requisite knowledge, confidence, and ethical awareness to navigate this evolving landscape [3].”
The missing value of medical artificial intelligence
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Artificial intelligence virtual extracellular vesicles (AIVEVs)
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“Meanwhile, the rise of artificial intelligence (AI) has also significantly advanced computational biology [9,10].”
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“113,114 Further, AI models require certain development and maintenance costs, and AI systems need to continuously update data and algorithms.”
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A multi-model longitudinal assessment of ChatGPT performance on medical residency examinations
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Patient safety in AI-powered diagnostic pathology
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Artificial Intelligence and Youth: Cognitive, Educational, and Behavioral Impacts
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Toward artificial general intelligence in health care
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PSMA-Targeted radiopharmaceuticals: from diagnosis to risk & quality of life
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The Role of Artificial Intelligence and Big Data for Gastrointestinal Disease
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“As we contemplate the impact of AI-augmented clinical care, we must consider which of our values, if any, are represented in the AI programs providing this augmentation[4].”
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Transfer Learning-Based Super-Resolution for High-Precision Medical Imaging
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“Rapid advancements in Artificial Intelligence (AI) technologies have significantly impacted various industries, with the medical field being one of the primary beneficiaries [1], [2], [3], [4], [5].”
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“This is all more important considering that human values and biases are able to directly or indirectly influence the output of AI ‐ based models [12].”
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Safety and security of large language models in healthcare
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“Through LLMs, institutions and LLM providers could impose their own goals on a process that was previously out of their reach 137 .”
A Perspective on Artificial Intelligence for Molecular Pathologists
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Artificial Intelligence in Periodontology: The Past, the Present, the Future
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Challenges, Limitations, and Ethical Considerations of AI in Immunology and Healthcare
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Generative Artificial Intelligence in Nutrition: A Revolution in Accessibility and Personalization
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“Incorporating human values into AI models from the initial model development stage may help to reduce bias in the models (yu et al., 2024).”
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Advances in artificial intelligence in prostate cancer pathology
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Die Zukunft der Medizin: ein informierter Blick in die „Glaskugel“
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Studying the Role of Medicine in Nazism and the Holocaust to Shape Professional Identity Formation
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Harnessing Large Language Models to Promote Attainment of ACGME Competencies in Child Psychiatry
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Ultrasound-based artificial intelligence for breast lesion classification
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A Hybrid Ensemble Framework for Rare Event Detection in Large-Scale Tabular Data
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Artificial Intelligence in Practice: BCBAs’ Attitudes, Concerns, and Needs
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Current Trends in AI Ethics for Software as a Medical Device (SaMD)
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Kunstig intelligens kan ikke utføre kvalitativ analyse
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- 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
- Social Sciences › Safety Research › 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
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 ›
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