Language
English
Publication Date
9-1-2024
Journal
Journal of the American Medical Informatics Association
DOI
10.1093/jamia/ocae055
PMID
38511501
PMCID
PMC11339521
PubMedCentral® Posted Date
3-20-2024
PubMedCentral® Full Text Version
Post-print
Abstract
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.
Keywords
Health Equity, Humans, United States, Delivery of Health Care, Healthcare Disparities, Language, health equity, health disparities, digital inclusion, artificial intelligence
Published Open-Access
yes
Recommended Citation
Jorge A Rodriguez, Emily Alsentzer, and David W Bates, "Leveraging Large Language Models to Foster Equity in Healthcare" (2024). Faculty, Staff and Students Publications. 8424.
https://digitalcommons.library.tmc.edu/baylor_docs/8424