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Clinical Accuracy, Relevance, Clarity, and Emotional Sensitivity of Large Language Models to Surgical Patient Questions: Cross-Sectional Study.
- Source :
-
JMIR formative research [JMIR Form Res] 2024 Jun 07; Vol. 8, pp. e56165. Date of Electronic Publication: 2024 Jun 07. - Publication Year :
- 2024
-
Abstract
- 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.<br /> (©Mert Marcel Dagli, Felix Conrad Oettl, Jaskeerat Gujral, Kashish Malhotra, Yohannes Ghenbot, Jang W Yoon, Ali K Ozturk, William C Welch. Originally published in JMIR Formative Research (https://formative.jmir.org), 07.06.2024.)
Details
- Language :
- English
- ISSN :
- 2561-326X
- Volume :
- 8
- Database :
- MEDLINE
- Journal :
- JMIR formative research
- Publication Type :
- Academic Journal
- Accession number :
- 38848553
- Full Text :
- https://doi.org/10.2196/56165