The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Sociodemographic biases in medical decision making by large language models
Abstract
Large language models (LLMs) show promise in healthcare, but concerns remain that they may produce medically unjustified clinical care recommendations reflecting the influence of patients' sociodemographic characteristics. We evaluated nine LLMs, analyzing over 1.7 million model-generated outputs from 1,000 emergency department cases (500 real and 500 synthetic). Each case was presented in 32 variations (31 sociodemographic groups plus a control) while holding clinical details constant. Compared to both a physician-derived baseline and each model's own control case without sociodemographic identifiers, cases labeled as Black or unhoused or identifying as LGBTQIA+ were more frequently directed toward urgent care, invasive interventions or mental health evaluations. For example, certain cases labeled as being from LGBTQIA+ subgroups were recommended mental health assessments approximately six to seven times more often than clinically indicated. Similarly, cases labeled as having high-income status received significantly more recommendations (P < 0.001) for advanced imaging tests such as computed tomography and magnetic resonance imaging, while low- and middle-income-labeled cases were often limited to basic or no further testing. After applying multiple-hypothesis corrections, these key differences persisted. Their magnitude was not supported by clinical reasoning or guidelines, suggesting that they may reflect model-driven bias, which could eventually lead to health disparitie…
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“Directional patterns vary: clinical settings showed bias favoring higher-income patients [4, 70], while simulated college admissions favored low-SES applicants [66].”
From All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs? (2026) · cites this paper as [70]
In the citing paper
“Prior work on occupation-related LLM behavior has found context-dependent patterns: clinical settings showed models favoring higher-income patients [4, 70], while simulated college admissions showed the reverse [66].”
From All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs? (2026) · cites this paper as [70]
In the citing paper
“Our findings are consistent with the clinical pattern.”
From All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs? (2026) · cites this paper as [70]
In the citing paper
“This consistent null contrasts with prior work detecting gender effects in resume screening[3], clinical contexts [70], and deliberate attempts to elicit biased outputs [54].”
From All Public Voices Are Equal, But Are Some More Equal Than Others to LLMs? (2026) · cites this paper as [70]
Citations
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Cited by
Sorted by citations
Omar, M., Sorin, V., Collins, J. D., Reich, D., Freeman, R., Gavin, N., Charney, A., Stump, L., Bragazzi, N. L., Nadkarni, G. N., et al. (2025) · Communications Medicine · doi:10.1038/s43856-025-01021-3 · Cited by 204
“To automate and streamline vignette drafting, we used Anthropic ’ s Claude Sonnet 3.5 on a case-by-case basis with a structured few-shot prompt that contained two physician-written templates — one short and one long, similar to our prior validated casegenerationpipelinein NatureMedicine 16 .”
InfluentialSelf-citationArtsi, Y., Sorin, V., Glicksberg, B. S., Korfiatis, P. D., Nadkarni, G. N., & Klang, E. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1659134 · Cited by 113
Self-citationChatGPT Health performance in a structured test of triage recommendations
Ramaswamy, A., Tyagi, A., Hugo, H., Jiang, J. J., Jayaraman, P., Jangda, M., Te, A. E., Kaplan, S. A., Lampert, J., Freeman, R., et al. (2026) · Nature Medicine · doi:10.1038/s41591-026-04297-7 · Cited by 74
Self-citationGuo, S., Liu, D., Fang, X., Meng, Y. G., Zhou, Z.-Z., Li, J., Li, M., Luo, L. Q., Li, H.-L., Cai, X.-Y., et al. (2025) · International Journal of Surgery · doi:10.1097/js9.0000000000003668 · Cited by 83
Bouguettaya, A., Stuart, E. M., & Aboujaoude, E. N. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01746-4 · Cited by 60
Metadata
- Authors
- Omar, M., Soffer, S., Agbareia, R., Bragazzi, N. L., Apakama, D. U., Horowitz, C. R., Charney, A. W., Freeman, R., Kummer, B., Glicksberg, B. S., Nadkarni, G. N., & Klang, E.
- Affiliations
- Mount Sinai Health System (US)
- Rabin Medical Center (IL)
- Hadassah Medical Center (IL)
- Institut für Schlaganfall- und Demenzforschung (DE)
- Icahn School of Medicine at Mount Sinai (US)
- Corresponding author
- Omar, Mahmud
- Nadkarni, Girish N.
- Klang, Eyal
- ORCID
- Venue
- Nature Medicine
- Published
- 2025-04-07
- Volume
- 31
- Issue
- 6
- Pages
- 1873-1881
- ISSN
- 1078-8956 (ISSN-L)
- 1546-170X
- OpenAlex
- W4409210035
- Cited by
- 173
- References
- 38
- Citations by year
- 2026: 121 · 2025: 52
- FWCI
- 41.64
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Nature Medicine (publishedVersion)
- PubMed (publishedVersion)
- 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 › Machine Learning in Healthcare
- Keywords
- large language models, algorithmic bias, sociodemographic bias, emergency department care, health disparities, racial bias, LGBTQ health, homelessness, socioeconomic status, bias mitigation
- MeSH terms
- Clinical Decision-Making, Sociodemographic Factors, Large Language Models, Adult, Emergency Service, Hospital, Female, Humans, Language, Male, Middle Aged, Socioeconomic Factors, Bias
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