%0 Journal Article
%@ 2561-326X
%I JMIR Publications
%V 8
%N 
%P e56165
%T Clinical Accuracy, Relevance, Clarity, and Emotional Sensitivity of Large Language Models to Surgical Patient Questions: Cross-Sectional Study
%A Dagli,Mert Marcel
%A Oettl,Felix Conrad
%A Gujral,Jaskeerat
%A Malhotra,Kashish
%A Ghenbot,Yohannes
%A Yoon,Jang W
%A Ozturk,Ali K
%A Welch,William C
%+ Department of Neurosurgery, University of Pennsylvania Perelman School of Medicine, 801 Spruce Street, Philadelphia, PA, 19106, United States, 1 2672306493, marcel.dagli@pennmedicine.upenn.edu
%K artificial intelligence
%K AI
%K natural language processing
%K NLP
%K large language model
%K LLM
%K generative AI
%K cross-sectional study
%K health information
%K patient education
%K clinical accuracy
%K emotional sensitivity
%K surgical patient
%K surgery
%K surgical
%D 2024
%7 7.6.2024
%9 Research Letter
%J JMIR Form Res
%G English
%X This cross-sectional study evaluates the clinical accuracy, relevance, clarity, and emotional sensitivity of responses to inquiries from patients undergoing surgery provided by large language models (LLMs), highlighting their potential as adjunct tools in patient communication and education. Our findings demonstrated high performance of LLMs across accuracy, relevance, clarity, and emotional sensitivity, with Anthropic’s Claude 2 outperforming OpenAI’s ChatGPT and Google’s Bard, suggesting LLMs’ potential to serve as complementary tools for enhanced information delivery and patient-surgeon interaction.
%M 38848553
%R 10.2196/56165
%U https://formative.jmir.org/2024/1/e56165
%U https://doi.org/10.2196/56165
%U http://www.ncbi.nlm.nih.gov/pubmed/38848553