ArticleJournal of healthcare informatics research2025
Enhancing Patient-Physician Communication: Simulating African American Vernacular English in Medical Diagnostics with Large Language Models.
Article in Journal of healthcare informatics research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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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.
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Who cites it
2 citing papers in PubMed.
- Applying Large Language Models in Perioperative Medicine With an Equity-Informed Perspective.Anesthesia and analgesia · 2026Article
- Artificial Intelligence-powered tiered early warning framework addressing high false alarm rates for in-hospital mortality prediction.NPJ digital medicine · 2026Article
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Authors and funding
3 authors.
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Abstract
Effective communication is crucial in reducing health disparities. However, linguistic differences, such as African American Vernacular English (AAVE), can lead to communication gaps between patients and physicians, negatively affecting care and outcomes. This study examines whether large language models (LLMs), specifically GPT-4 and Llama 3.3, can replicate AAVE in simulated clinical dialogues to improve cultural sensitivity. We tested four prompt types-BaseP, DemoP, LingP, and CompP-using United States Medical Licensing Examination (USMLE) case simulations. Statistical analyses on the models' outputs showed a significant difference among prompt types for both GPT-4 ( Supplementary Information: The online version contains supplementary material available at 10.1007/s41666-025-00194-9.
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