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

Journals

  1. Brin D, Sorin V, Konen E, Nadkarni G, Glicksberg B, Klang E. How GPT models perform on the United States medical licensing examination: a systematic review. Discover Applied Sciences 2024;6(10) View
  2. Dagli M, Ghenbot Y, Ahmad H, Chauhan D, Turlip R, Wang P, Welch W, Ozturk A, Yoon J. Development and validation of a novel AI framework using NLP with LLM integration for relevant clinical data extraction through automated chart review. Scientific Reports 2024;14(1) View
  3. Mahamid A, Laver L, Zahalka S, Oettl F, Behrbalk E, Hirschmann M, Samuelsson K. Editorial – Current capacities and future possibilities of large language models in orthopaedic surgery. Journal of Experimental Orthopaedics 2025;12(2) View
  4. Wang C, Zhu Y, Zhang X, Chen X, Li Y, Tan Y, Qi H. Areas of research focus and trends in the research on the application of AIGC in healthcare. Journal of Health, Population and Nutrition 2025;44(1) View
  5. Masur L, Driller M, Suppiah H, Matzka M, Sperlich B, Düking P. Assessment of Recommendations Provided to Athletes Regarding Sleep Education by GPT-4o and Google Gemini: Comparative Evaluation Study. JMIR Formative Research 2025;9:e71358 View