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Accepted for/Published in: JMIR Formative Research

Date Submitted: Jan 8, 2024
Open Peer Review Period: Jan 11, 2024 - Mar 7, 2024
Date Accepted: Apr 23, 2024
(closed for review but you can still tweet)

The final, peer-reviewed published version of this preprint can be found here:

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

Dagli MM, Oettl FC, Gujral J, Malhotra K, Ghenbot Y, Yoon JW, Ozturk AK, Welch WC

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

JMIR Form Res 2024;8:e56165

DOI: 10.2196/56165

PMID: 38848553

PMCID: 11193072

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

  • Mert Marcel Dagli; 
  • Felix Conrad Oettl; 
  • Jaskeerat Gujral; 
  • Kashish Malhotra; 
  • Yohannes Ghenbot; 
  • Jang W Yoon; 
  • Ali K Ozturk; 
  • William C Welch

ABSTRACT

This cross-sectional study evaluates the clinical accuracy, relevance, clarity, and emotional sensitivity of responses to surgical patient inquiries provided by Large Language Models, highlighting their potential as adjunct tools in patient communication and education.


 Citation

Please cite as:

Dagli MM, Oettl FC, Gujral J, Malhotra K, Ghenbot Y, Yoon JW, Ozturk AK, Welch WC

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

JMIR Form Res 2024;8:e56165

DOI: 10.2196/56165

PMID: 38848553

PMCID: 11193072

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