Leveraging large language models to foster equity in healthcare
Open accessAbstract
OBJECTIVES: Large language models (LLMs) are poised to change care delivery, but their impact on health equity is unclear. While marginalized populations have been historically excluded from early technology developments, LLMs present an opportunity to change our approach to developing, evaluating, and implementing new technologies. In this perspective, we describe the role of LLMs in supporting health equity. MATERIALS AND METHODS: We apply the National Institute on Minority Health and Health Disparities (NIMHD) research framework to explore the use of LLMs for health equity. RESULTS: We present opportunities for how LLMs can improve health equity across individual, family and organizational, community, and population health. We describe emerging concerns including biased data, limited technology diffusion, and privacy. Finally, we highlight recommendations focused on prompt engineering, retrieval augmentation, digital inclusion, transparency, and bias mitigation. CONCLUSION: The potential of LLMs to support health equity depends on making health equity a focus from the start.
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Some studies evaluated LLMs in multiple languages, highlighting a potential role for multilingual LLMs in supporting knowledge transfer and improving access to healthcare information across diverse linguistic and cultural contexts.
“some studies evaluated LLMs in multiple languages, highlighting a potential role for multilingual LLMs in supporting J o u r n a l P r e -p r o o f knowledge transfer and improving access to healthcare information across diverse linguistic and cultural contexts.2”
From Large Language Models in Pharmacist-Led Direct Patient Care: A Rapid Review of Published Evaluations (2026) · cites this paper as [2]
The Community health section describes potential LLM support for patient messages in multiple languages, and the section on retrieval augmentation and fine-tuning discusses culturally and linguistically diverse health information. These passages support the proposed role in improving access, but the paper does not report that studies evaluated LLMs in multiple languages or establish the claimed knowledge-transfer role. (LLM, conf=0.85)
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- Authors
- Rodriguez, J. A., Alsentzer, E., & Bates, D. W.
- Affiliations
- Brigham and Women's Hospital (US)
- Corresponding author
- Rodriguez, Jorge A
- Venue
- Journal of the American Medical Informatics Association
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-03-20
- Volume
- 31
- Issue
- 9
- Pages
- 2147-2150
- ISSN
- 1067-5027 (ISSN-L)
- 1527-974X
- PMC
- PMC11339521
- OpenAlex
- W4394760744
- Cited by
- 32
- References
- 19
- Citations by year
- 2026: 8 · 2025: 19 · 2024: 5
- FWCI
- 2.98
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Journal of the American Medical Informatics Association (publishedVersion)
- PubMed Central (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed (publishedVersion)
- Digital Commons-TMC (Texas Medical Center) (submittedVersion)
- Funders
- National Institute of Minority Health and Health Disparities (K23MD016439)
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Social Sciences › Sociology and Political Science › Cultural Competency in Health Care
- Keywords
- large language models, health equity, health disparities, algorithmic bias, data bias, technology diffusion, privacy, prompt engineering, retrieval augmentation, digital inclusion, transparency, bias mitigation, marginalized communities, health technology development
- MeSH terms
- Health Equity, Delivery of Health Care, Humans, Language, United States, Healthcare Disparities
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