This paper is in the following e-collection/theme issue:

Generative Language Models Including ChatGPT (492) Artificial Intelligence (1723) Chatbots and Conversational Agents (627) Systemic Racism and Racial Bias in Health Care and Society (98) Equity and Digital Divide (278) HIV/AIDS/STI Prevention and Care (943)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57257, first published .
Assessing Racial and Ethnic Bias in Text Generation by Large Language Models for Health Care–Related Tasks: Cross-Sectional Study

Assessing Racial and Ethnic Bias in Text Generation by Large Language Models for Health Care–Related Tasks: Cross-Sectional Study

Assessing Racial and Ethnic Bias in Text Generation by Large Language Models for Health Care–Related Tasks: Cross-Sectional Study

Authors of this article:

John J Hanna1, 2, 3 Author Orcid Image ;   Abdi D Wakene3 Author Orcid Image ;   Andrew O Johnson1 Author Orcid Image ;   Christoph U Lehmann3, 4 Author Orcid Image ;   Richard J Medford1, 2, 3 Author Orcid Image
Article Authors Cited by Tweetations Metrics Author Video

John J Hanna   1, 2, 3 , MD ;   Abdi D Wakene   3 , BMSc ;   Andrew O Johnson   1 , PhD ;   Christoph U Lehmann   3, 4 , MD ;   Richard J Medford   1, 2, 3 , MD

1 Information Services, ECU Health, Greenville, NC, United States

2 Division of Infectious Diseases, Department of Internal Medicine, East Carolina University, Greenville, NC, United States

3 Clinical Informatics Center, University of Texas Southwestern, Dallas, TX, United States

4 Department of Pediatrics, University of Texas Southwestern, The University of Texas Southwestern Medical Center, Dallas, TX, United States

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