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

Theme Issue: ChatGPT and Generative Language Models in Medical Education (81) Generative Language Models Including ChatGPT (280) Artificial Intelligence (AI) in Medical Education (71) Clinical Information and Decision Making (1367) Clinical Informatics (969) Computer-Aided Diagnosis (114) Decision Support for Health Professionals (1158) Chatbots and Conversational Agents (509) Validity, Reliability, and Bias (91)

Published on in Vol 9 (2023)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/47532, first published .
The Accuracy and Potential Racial and Ethnic Biases of GPT-4 in the Diagnosis and Triage of Health Conditions: Evaluation Study

The Accuracy and Potential Racial and Ethnic Biases of GPT-4 in the Diagnosis and Triage of Health Conditions: Evaluation Study

The Accuracy and Potential Racial and Ethnic Biases of GPT-4 in the Diagnosis and Triage of Health Conditions: Evaluation Study

Authors of this article:

Naoki Ito1, 2 Author Orcid Image ;   Sakina Kadomatsu1, 3 Author Orcid Image ;   Mineto Fujisawa1, 2 Author Orcid Image ;   Kiyomitsu Fukaguchi1, 4 Author Orcid Image ;   Ryo Ishizawa1, 5 Author Orcid Image ;   Naoki Kanda1, 6 Author Orcid Image ;   Daisuke Kasugai1, 7 Author Orcid Image ;   Mikio Nakajima1, 8 Author Orcid Image ;   Tadahiro Goto1 Author Orcid Image ;   Yusuke Tsugawa9, 10 Author Orcid Image
Article Authors Cited by (10) Tweetations (18) Metrics

Journals

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  5. Mathis W, Zhao S, Pratt N, Weleff J, De Paoli S. Inductive thematic analysis of healthcare qualitative interviews using open-source large language models: How does it compare to traditional methods?. Computer Methods and Programs in Biomedicine 2024;255:108356 View
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  9. Agrawal A. Fairness in AI-Driven Oncology: Investigating Racial and Gender Biases in Large Language Models. Cureus 2024 View