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

Research Letter (12) Chatbots and Conversational Agents (621) Generative Language Models Including ChatGPT (477) Artificial Intelligence (1698) Natural Language Processing (793) Development and Evaluation of Research Methods, Instruments and Tools (630) Mobile tools for surgery and perioperative medicine (95) Cross-Sectional Studies in Public Health (334)

Published on in Vol 8 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/56165, first published .
Clinical Accuracy, Relevance, Clarity, and Emotional Sensitivity of Large Language Models to Surgical Patient Questions: Cross-Sectional Study

Clinical Accuracy, Relevance, Clarity, and Emotional Sensitivity of Large Language Models to Surgical Patient Questions: Cross-Sectional Study

Clinical Accuracy, Relevance, Clarity, and Emotional Sensitivity of Large Language Models to Surgical Patient Questions: Cross-Sectional Study

Authors of this article:

Mert Marcel Dagli1 Author Orcid Image ;   Felix Conrad Oettl2, 3 Author Orcid Image ;   Jaskeerat Gujral1 Author Orcid Image ;   Kashish Malhotra4 Author Orcid Image ;   Yohannes Ghenbot1 Author Orcid Image ;   Jang W Yoon1 Author Orcid Image ;   Ali K Ozturk1 Author Orcid Image ;   William C Welch1 Author Orcid Image
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JMIR Formative Res: Clinical Accuracy, Relevance, Clarity, and Emotional Sensitivity of Large Language Models to Surgical Patient Questions: Cross-Sectional Study https://t.co/C0gS5RdwJY https://t.co/jqVeaaSrGA

5:31 PM · Jun 07, 2024

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RT @jmirpub: JMIR Formative Res: Clinical Accuracy, Relevance, Clarity, and Emotional Sensitivity of Large Language Models to Surgical Pati…

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