ArticleJournal of the American Medical Informatics Association : JAMIA2024
Leveraging large language models to foster equity in healthcare.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Evaluating and addressing demographic disparities in medical large language models: a systematic review.International journal for equity in health · 2025Pooled it
- Sex and gender bias in large language models: an old problem at a new scale.BMJ health & care informatics · 2026Article
- Ethical Considerations in Personal Health Large Language Models.Journal of medical Internet research · 2026Article
- Cultivating AI leaders: the role of clinical informatics fellowship in transforming healthcare.Lancet regional health. Americas · 2026Article
- Hepatitis C Patient Education: Large Language Models Show Promise in Disseminating Guidelines.Journal of clinical and translational hepatology · 2026Article
- Artificial Intelligence in Addressing Interpersonal Violence Against Vulnerable Populations: A Scoping Review.Revista brasileira de enfermagem · 2026Article
- Identifying Bias at Scale in Clinical Notes Using Large Language Models.Mayo Clinic proceedings. Digital health · 2025Article
- Enhancing Adverse Event Reporting With Clinical Language Models: Inpatient Falls.Journal of advanced nursing · 2025Observational
- [Proposal for Responsible Use of Generative Artificial Intelligence in Medical Practice].Revista de neurologia · 2025Review
- Evaluating the role of large language models in traditional Chinese medicine diagnosis and treatment recommendations.NPJ digital medicine · 2025Article
- Clinical trials informed framework for real world clinical implementation and deployment of artificial intelligence applications.NPJ digital medicine · 2025Review
- Future horizons in diabetes: integrating AI and personalized care.Frontiers in endocrinology · 2025Review
- Not Replaced, but Reinvented: AI Education Pathways to Prepare Future Physicians to Lead Healthcare Transformation.Perspectives on medical education · 2025Article
- Large Language Models in Worldwide Medical Exams: Platform Development and Comprehensive Analysis.Journal of medical Internet research · 2024Article
- Large language models in biomedicine and health: current research landscape and future directions.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Zero-shot learning to extract assessment criteria and medical services from the preventive healthcare guidelines using large language models.Journal of the American Medical Informatics Association : JAMIA · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
objectivesLarge 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
methodsWe apply the National Institute on Minority Health and Health Disparities (NIMHD) research framework to explore the use of LLMs for health equity.
resultsWe 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.
conclusionThe potential of LLMs to support health equity depends on making health equity a focus from the start.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.