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Journal article
Enhancing Patient-Physician Communication: Simulating African American Vernacular English in Medical Diagnostics with Large Language Models
Journal of healthcare informatics research
11 Mar 2025
Featured in Collection : Research Supported by Drexel Libraries' OA Programs
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 including demographic information and/or explicit AAVE cues influences each model’s output. Combining demographic and linguistic cues (CompP) yielded the highest mean AAVE feature counts (e.g., 9.83 for GPT-4 vs. 16.06 for Llama 3.3), although neither model fully captured the diversity of AAVE. Moreover, simply mentioning African American demographics triggers extra informal forms, suggesting built-in stereotypes or biases in both models. Overall, these findings highlight the promise of LLMs for culturally sensitive healthcare communication, while underscoring the need for continued refinement to address stereotypes and more accurately represent diverse linguistic styles.
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Details
- Title
- Enhancing Patient-Physician Communication: Simulating African American Vernacular English in Medical Diagnostics with Large Language Models
- Creators
- Yeawon Lee - Drexel UniversityChia-Hsuan Chang - Yale UniversityChristopher C Yang (Corresponding Author) - Drexel University, Information Science
- Publication Details
- Journal of healthcare informatics research
- Publisher
- Springer Nature
- Number of pages
- 35
- Grant note
- National Science FoundationDepartment of Defense: DoD W91XWH-05-1-023
This work was supported in part by the National Science Foundation under the Grants IIS-1741306 and IIS-2235548, and by the Department of Defense under the Grant DoD W91XWH-05-1-023. This material is based upon work supported by (while serving at) the National Science Foundation. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Information Science
- Web of Science ID
- WOS:001441619800001
- Scopus ID
- 2-s2.0-105003874731
- Other Identifier
- 991022032970604721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Information Systems
- Health Care Sciences & Services
- Medical Informatics