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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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Citations
Cites
Tversky, A., & Kahneman, D. (1974) · Science · doi:10.1126/science.185.4157.1124 · Cited by 28,810
A New Equation to Estimate Glomerular Filtration Rate
Levey, A. S., Stevens, L. A., Schmid, C. H., Zhang, Y. (., Castro, A. F., Feldman, H. I., Kusek, J. W., Eggers, P., Van Lente, F., Greene, T., et al. (2009) · Annals of Internal Medicine · doi:10.7326/0003-4819-150-9-200905050-00006 · Cited by 25,603
Language Models are Few-Shot Learners
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al. (2020) · arXiv · doi:10.48550/arxiv.2005.14165 · Cited by 23,372
Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., Filiberti, A., Flechtner, H., Fleishman, S. B., Haes, J. C. J. M. D., et al. (1993) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/85.5.365 · Cited by 16,005
Levey, A. S., Bosch, J. P., Lewis, J. B., Greene, T., Rogers, N., Roth, D., & for the Modification of Diet in Renal Disease Study Group* (1999) · Annals of Internal Medicine · doi:10.7326/0003-4819-130-6-199903160-00002 · Cited by 15,079
KDIGO 2024 clinical practice guideline for the evaluation and management of chronic kidney disease
Stevens, P. E., Ahmed, S. B., Carrero, J. J., Foster, B., Francis, A., Hall, R. K., Herrington, W. G., Hill, G., Inker, L. A., Kazancıoğlu, R., et al. (2024) · Kidney International · doi:10.1016/j.kint.2023.10.018 · Cited by 8,530
Dissecting racial bias in an algorithm used to manage the health of populations
Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019) · Science · doi:10.1126/science.aax2342 · Cited by 7,137
Decision curve analysis: a novel method for evaluating prediction models
Vickers, A. J., & Elkin, E. B. (2006) · Medical Decision Making · doi:10.1177/0272989x06295361 · Cited by 6,341
EQ-SD: a measure of health status from the EuroQol Group
Rabin, R., & Charro, F. D. (2001) · Annals of Medicine · doi:10.3109/07853890109002087 · Cited by 5,613
New creatinine- and cystatin C-based equations to estimate GFR without race
Inker, L. A., Eneanya, N. D., Coresh, J., Tighiouart, H., Wang, D., Sang, Y., Crews, D. C., Doria, A., Estrella, M. M., Froissart, M., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2102953 · Cited by 4,695
LLaMA: Open and Efficient Foundation Language Models
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., et al. (2023) · arXiv · doi:10.48550/arxiv.2302.13971 · Cited by 4,602
Training language models to follow instructions with human feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C. L., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · arXiv · doi:10.48550/arxiv.2203.02155 · Cited by 4,208
Large language models encode clinical knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2023) · Nature · doi:10.1038/s41586-023-06291-2 · Cited by 4,083
Large Language Models Encode Clinical Knowledge
Singhal, K., Azizi, S., Tu, T., Mahdavi, S. S., Wei, J., Chung, H. W., Scales, N., Tanwani, A., Cole-Lewis, H., Pfohl, S., et al. (2022) · arXiv · doi:10.48550/arxiv.2212.13138 · Cited by 4,083
Training Language Models to Follow Instructions with Human Feedback
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · Advances in Neural Information Processing Systems 35 · doi:10.52202/068431-2011 · Cited by 3,117
Artificial intelligence in healthcare
Yu, K.-H., Beam, A. L., & Kohane, I. S. (2018) · Nature Biomedical Engineering · doi:10.1038/s41551-018-0305-z · Cited by 2,817
Self-citationModel Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681
Ayers, J. W., Poliak, A., Dredze, M., Leas, E. C., Zhu, Z., Kelley, J. B., Faix, D. J., Goodman, A. M., Longhurst, C. A., Hogarth, M., et al. (2023) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2023.1838 · Cited by 2,613
Measurement of health state utilities for economic appraisal
Torrance, G. W. (1986) · Journal of Health Economics · doi:10.1016/0167-6296(86)90020-2 · Cited by 2,220
Experimental evidence on the productivity effects of generative artificial intelligence
Noy, S., & Zhang, W. (2023) · Science · doi:10.1126/science.adh2586 · Cited by 2,150
A Difficulty in the Concept of Social Welfare
Arrow, K. J. (1950) · Journal of Political Economy · doi:10.1086/256963 · Cited by 2,079
Benefits, Limits, and Risks of GPT-4 as an AI Chatbot for Medicine
Lee, P., Bubeck, S., & Petro, J. (2023) · New England Journal of Medicine · doi:10.1056/nejmsr2214184 · Cited by 1,738
Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
Vyas, D. A., Eisenstein, L. G., & Jones, D. S. (2020) · New England Journal of Medicine · doi:10.1056/nejmms2004740 · Cited by 1,731
On the Elicitation of Preferences for Alternative Therapies
McNeil, B. J., Pauker, S. G., Sox, H. C., & Tversky, A. (1982) · New England Journal of Medicine · doi:10.1056/nejm198205273062103 · Cited by 1,683
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
The Health Utilities Index (HUI): concepts, measurement properties and applications.
Horsman, J., Furlong, W. J., Feeny, D. F., & Torrance, G. W. (2003) · Health and Quality of Life Outcomes · doi:10.1186/1477-7525-1-54 · Cited by 1,345
Primer on Certain Elements of Medical Decision Making
McNeil, B. J., Keeler, E., & Adelstein, S. J. (1975) · New England Journal of Medicine · doi:10.1056/nejm197507312930501 · Cited by 1,131
The imperative for regulatory oversight of large language models (or generative AI) in healthcare
Meskó, B., & Topol, E. J. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00873-0 · Cited by 1,047
Reasoning Foundations of Medical Diagnosis
Ledley, R. S., & Lusted, L. B. (1959) · Science · doi:10.1126/science.130.3366.9 · Cited by 998
Yu, K.-H., Zhang, C., Berry, G. J., Altman, R. B., Ré, C., Rubin, D. L., & Snyder, M. (2016) · Nature Communications · doi:10.1038/ncomms12474 · Cited by 972
Self-citationSeyyed-Kalantari, L., Zhang, H., McDermott, M. B. A., Chen, I. Y., & Ghassemi, M. (2021) · Nature Medicine · doi:10.1038/s41591-021-01595-0 · Cited by 928
The Clinician and Dataset Shift in Artificial Intelligence
Finlayson, S. G., Subbaswamy, A., Singh, K., Bowers, J., Kupke, A., Zittrain, J., Kohane, I. S., & Saria, S. (2021) · New England Journal of Medicine · doi:10.1056/nejmc2104626 · Cited by 855
Self-citationGenetic Misdiagnoses and the Potential for Health Disparities
Manrai, A. K., Funke, B. H., Rehm, H. L., Olesen, M. S., Maron, B. A., Szolovits, P., Margulies, D. M., Loscalzo, J., & Kohane, I. S. (2016) · New England Journal of Medicine · doi:10.1056/nejmsa1507092 · Cited by 808
Self-citationFitzgerald, M., Saville, B. R., & Lewis, R. (2015) · JAMA · doi:10.1001/jama.2015.37 · Cited by 789
Delgado, C., Baweja, M., Crews, D. C., Eneanya, N. D., Gadegbeku, C. A., Inker, L. A., Mendu, M. L., Miller, W. G., Moxey-Mims, M. M., Roberts, G. V., et al. (2022) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2021.08.003 · Cited by 783
Powell, R. A., & Single, H. M. (1996) · International Journal for Quality in Health Care · doi:10.1093/intqhc/8.5.499 · Cited by 707
Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond
Yang, J., Jin, H., Tang, R., Han, X., Feng, Q., Jiang, H., Yin, B., & Hu, X. (2023) · arXiv · doi:10.48550/arxiv.2304.13712 · Cited by 644
Zauber, A. G., Lansdorp‐Vogelaar, I., Knudsen, A. B., Wilschut, J. A., van Ballegooijen, M., & Kuntz, K. M. (2008) · Annals of Internal Medicine · doi:10.7326/0003-4819-149-9-200811040-00244 · Cited by 580
Potential liability for physicians using artificial intelligence
Price, W. N., Gerke, S., & Cohen, I. G. (2019) · JAMA · doi:10.1001/jama.2019.15064 · Cited by 579
Focus group discussion: a tool for health and medical research.
Wong, L. P. (2008) · PubMed · Cited by 566
Disparities in dermatology AI performance on a diverse, curated clinical image set
Daneshjou, R., Vodrahalli, K., Novoa, R. A., Jenkins, M., Liang, W., Rotemberg, V., Ko, J., Swetter, S. M., Bailey, E. E., Gevaert, O., et al. (2022) · Science Advances · doi:10.1126/sciadv.abq6147 · Cited by 544
The Nominal Group Technique: A Research Tool for General Practice?
Gallagher, M., Hares, T., SPENCER, J., Bradshaw, C., & WEBB, I. (1993) · Family Practice · doi:10.1093/fampra/10.1.76 · Cited by 538
Capabilities of GPT-4 on Medical Challenge Problems
Nori, H., King, N., McKinney, S. M., Carignan, D., & Horvitz, E. (2023) · arXiv · doi:10.48550/arxiv.2303.13375 · Cited by 509
Pauker, S. G., & Kassirer, J. P. (1987) · New England Journal of Medicine · doi:10.1056/nejm198701293160505 · Cited by 480
Categorical and probabilistic reasoning in medical diagnosis
Szolovits, P., & Pauker, S. G. (1978) · Artificial Intelligence · doi:10.1016/0004-3702(78)90014-0 · Cited by 409
Reconsidering the Consequences of Using Race to Estimate Kidney Function
Eneanya, N. D., Yang, W., & Reese, P. P. (2019) · JAMA · doi:10.1001/jama.2019.5774 · Cited by 385
Challenges to the Reproducibility of Machine Learning Models in Health Care
Beam, A. L., Manrai, A. K., & Ghassemi, M. (2020) · JAMA · doi:10.1001/jama.2019.20866 · Cited by 366
Self-citationTowards Expert-Level Medical Question Answering with Large Language Models
Singhal, K., Tu, T., Gottweis, J., Sayres, R., Wulczyn, E., Hou, L., Clark, K., Pfohl, S., Cole-Lewis, H., Neal, D., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.09617 · Cited by 338
Measured and estimated glomerular filtration rate: current status and future directions
Levey, A. S., Coresh, J., Tighiouart, H., Greene, T., & Inker, L. A. (2019) · Nature Reviews Nephrology · doi:10.1038/s41581-019-0191-y · Cited by 336
An algorithmic approach to reducing unexplained pain disparities in underserved populations
Pierson, E., Cutler, D. M., Leskovec, J., Mullainathan, S., & Obermeyer, Z. (2021) · Nature Medicine · doi:10.1038/s41591-020-01192-7 · Cited by 268
Kidney Disease, Race, and GFR Estimation
Levey, A. S., Titan, S., Powe, N. R., Coresh, J., & Inker, L. A. (2020) · Clinical Journal of the American Society of Nephrology · doi:10.2215/cjn.12791019 · Cited by 262
Framing the challenges of artificial intelligence in medicine
Yu, K.-H., & Kohane, I. S. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008551 · Cited by 233
Self-citationUse of discrete choice experiments to elicit preferences.
Ryan, M., Bate, A., EASTMOND, C. J., & Ludbrook, A. (2001) · PubMed · doi:10.1136/qhc.0100055 · Cited by 221
The Challenges for Regulating Medical Use of ChatGPT and Other Large Language Models
Minssen, T., Vayena, E., & Cohen, I. G. (2023) · JAMA · doi:10.1001/jama.2023.9651 · Cited by 171
Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology
Agarwal, N., Moehring, A., Rajpurkar, P., & Salz, T. (2023) · doi:10.3386/w31422 · Cited by 161
Combining Human Expertise with Artificial Intelligence: Experimental Evidence from Radiology
Agarwal, N., Moehring, A., Rajpurkar, P., & Salz, T. (2023) · doi:10.2139/ssrn.4505053 · Cited by 161
Ahmed, S., Nutt, C. T., Eneanya, N. D., Reese, P. P., Sivashanker, K., Morse, M., Sequist, T., & Mendu, M. L. (2021) · Journal of General Internal Medicine · doi:10.1007/s11606-020-06280-5 · Cited by 147
Uses of GFR and Albuminuria Level in Acute and Chronic Kidney Disease
Levey, A. S., Grams, M. E., & Inker, L. A. (2022) · New England Journal of Medicine · doi:10.1056/nejmra2201153 · Cited by 145
Valuing SF-6D Health States Using a Discrete Choice Experiment
Norman, R., Viney, R. C., Brazier, J. E., Burgess, L., Cronin, P., King, M. T., Ratcliffe, J., & Street, D. J. (2013) · Medical Decision Making · doi:10.1177/0272989x13503499 · Cited by 143
Considering Biased Data as Informative Artifacts in AI-Assisted Health Care
Ferryman, K. S., Mackintosh, M. E., & Ghassemi, M. (2023) · New England Journal of Medicine · doi:10.1056/nejmra2214964 · Cited by 142
O'Meara, J. J., Mcnutt, R., Evans, A. T., Moore, S. W., & Downs, S. M. (1994) · New England Journal of Medicine · doi:10.1056/nejm199406303302605 · Cited by 135
Clinical Implications of Removing Race From Estimates of Kidney Function
Diao, J. A., Wu, G. J., Taylor, H. A., Tucker, J. K., Powe, N. R., Kohane, I. S., & Manrai, A. K. (2020) · JAMA · doi:10.1001/jama.2020.22124 · Cited by 125
Self-citationCorrecting Biases in Standard Gamble and Time Tradeoff Utilities
van Osch, S. M. C., Wakker, P. P., van den Hout, W. B., & Stiggelbout, A. M. (2004) · Medical Decision Making · doi:10.1177/0272989x04268955 · Cited by 122
Capabilities of Gemini Models in Medicine
Saab, K., Tu, T., Weng, W.-H., Tanno, R., Stutz, D., Wulczyn, E., Zhang, F., Strother, T., Park, C., Vedadi, E., et al. (2024) · arXiv · doi:10.48550/arxiv.2404.18416 · Cited by 117
In Search of a Better Equation — Performance and Equity in Estimates of Kidney Function
Diao, J. A., Inker, L. A., Levey, A. S., Tighiouart, H., Powe, N. R., & Manrai, A. K. (2021) · New England Journal of Medicine · doi:10.1056/nejmp2028243 · Cited by 115
Self-citationTowards Conversational Diagnostic AIPreprint
Tu, T., Palepu, A. K., Schaekermann, M., Saab, K. K., Freyberg, J., Tanno, R., Wang, A., Li, B., Amin, M., Tomašev, N., et al. (2024) · arXiv (Cornell University) · doi:10.48550/arxiv.2401.05654 · Cited by 114
Lugnér, A. K., & Krabbe, P. F. M. (2020) · Expert Review of Pharmacoeconomics & Outcomes Research · doi:10.1080/14737167.2020.1779062 · Cited by 99
In the Era of Precision Medicine and Big Data, Who Is Normal?
Manrai, A. K., Patel, C. J., & Ioannidis, J. P. A. (2018) · JAMA · doi:10.1001/jama.2018.2009 · Cited by 98
Self-citationYu, K., Lee, M. T., Yen, M.-H., Kou, S. C., Rosen, B. R., Chiang, J.-H., & Kohane, I. S. (2020) · Journal of Medical Internet Research · doi:10.2196/16709 · Cited by 80
Self-citationProbing the psychology of AI models
Shiffrin, R., & Mitchell, M. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2300963120 · Cited by 70
Removing Race From Kidney Function Estimates—Reply
Diao, J. A., Powe, N. R., & Manrai, A. K. (2021) · JAMA · doi:10.1001/jama.2021.3461 · Cited by 68
Self-citationMachine learning for cryosection pathology predicts the 2021 WHO classification of glioma
Nasrallah, M. P., Zhao, J., Tsai, C. C., Meredith, D., Marostica, E., Ligon, K. L., Golden, J. A., & Yu, K.-H. (2023) · Med · doi:10.1016/j.medj.2023.06.002 · Cited by 67
Self-citationTo Do No Harm — and the Most Good — with AI in Health Care
Goldberg, C. B., Adams, L. L., Blumenthal, D., Brennan, P. F., Brown, N., Butte, A. J., Cheatham, M., deBronkart, D., Dixon, J., Drazen, J. M., et al. (2024) · NEJM AI · doi:10.1056/aip2400036 · Cited by 61
Self-citationUsing Measures of Race to Make Clinical Predictions: Decision Making, Patient Health, and Fairness
Manski, C. F., Mullahy, J., & Venkataramani, A. S. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2303370120 · Cited by 52
Transforming Global Health with AI
Mehta, M. C., Katz, I. T., & Jha, A. K. (2020) · New England Journal of Medicine · doi:10.1056/nejmp1912079 · Cited by 40
Nong, P., Hamasha, R., Singh, K., Adler-Milstein, J., & Platt, J. (2024) · NEJM AI · doi:10.1056/aip2300048 · Cited by 34
Enabling collaborative governance of medical AI
Price, W. N., Sendak, M., Balu, S., & Singh, K. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00699-1 · Cited by 27
Accuracy and Equity in Clinical Risk Prediction
Pierson, E. (2024) · New England Journal of Medicine · doi:10.1056/nejmp2311050 · Cited by 18
Measuring Fairness, Inequality, and Big Data: Social Choice Since Arrow
Patty, J. W., & Penn, E. M. (2019) · Annual Review of Political Science · doi:10.1146/annurev-polisci-022018-024704 · Cited by 13
Ji, Y., & Gao, S. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.giscience.2023.43 · Cited by 10
Rodari, G., Profka, E., Giacchetti, F., Cavenaghi, I., Arosio, M., & Giavoli, C. (2021) · Scientific Reports · doi:10.1038/s41598-021-93963-6 · Cited by 8
OpenAI, Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F. L., Almeida, D., Altenschmidt, J., Altman, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.08774
Self-citation
Research works printed but not linked: 2
- Stats STAT! NEJM Evidence (https://evidence.nejm.org/browse/evidence-media-type/stats-stat).
- Mate A Madaan L Taneja A et al. Field study in deploying restless multi-armed bandits: assisting non-profits in improving maternal and child health. In: Proceedings and Abstracts of the 36th AAAI Conference on Artificial Intelligence February 22–March 1 2022. Virtual: Association for the Advancement of Artificial Intelligence 2022 (https://cdn.aaai.org/ojs/21460/21460-13-25473-1-2-20220628.pdf).
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Huang, Q., Li, Y., Huang, Y., Wu, J., Bao, W., Xue, C., Li, X., Dong, S., Dong, Z., & Hu, S. (2025) · Signal Transduction and Targeted Therapy · doi:10.1038/s41392-025-02243-6 · Cited by 197
El Arab, R. A., Abu-Mahfouz, M. S., Abuadas, F. H., Alzghoul, H., Almari, M., Ghannam, A., & Seweid, M. M. (2025) · Healthcare · doi:10.3390/healthcare13070701 · Cited by 108
“Despite regulatory frameworks such as GDPR, gaps persist in ensuring ethical data usage, especially in cross-border applications [29,31].”
Triola, M. M., & Rodman, A. (2025) · Academic Medicine · doi:10.1097/acm.0000000000005963 · Cited by 92
Implementing Artificial Intelligence in Critical Care Medicine: a consensus of 22.
Cecconi, M., Greco, M., Shickel, B., Angus, D. C., Bailey, H., Bignami, E., Calandra, T., Celi, L. A., Einav, S., Elbers, P., et al. (2025) · Critical Care · doi:10.1186/s13054-025-05532-2 · Cited by 80
“Consequently, AI development is led by major technology companies whose goals may not always align with those of patients or healthcare systems [10, 11].”
Demographic bias of expert-level vision-language foundation models in medical imaging
Yang, Y., Liu, Y., Liu, X., Gulhane, A., Mastrodicasa, D., Wu, W., Wang, E. J., Sahani, D. V., & Patel, S. (2025) · Science Advances · doi:10.1126/sciadv.adq0305 · Cited by 67
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
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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