Enhancing Patient-Physician Communication: Simulating African American Vernacular English in Medical Diagnostics with Large Language Models.

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 (F(2,70) = 6.218, p = 0.003) and Llama 3.3 (F(2,70) = 12.124, p < 0.001), indicating that inclu

SUBMITTER: Lee Y 

PROVIDER: S-EPMC12037967 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature