ArticleBMC medical informatics and decision making2025
Evaluation of large language models in patient education and clinical decision support for rotator cuff injury: a two-phase benchmarking study.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Diagnostic Performance of Large Language Models for Orthopedic-Related Rare Diseases and Their Impact on Physicians' Diagnostic Accuracy: 2-Stage Comparative Evaluation Study Based on the Chinese Rare Disease Catalog.Journal of medical Internet research · 2026Article
- Evaluation of Large Language Models in Infectious Disease Decision-Making: From Examination to Clinical Practice.Infection and drug resistance · 2026Article
- Comparing Chinese-language large language models for caregiver questions about developmental dysplasia of the hip.Frontiers in pediatrics · 2026Article
- ChatGPT as a source of surgical information: Evaluation of responses to patient questions on hallux rigidus fusion.Digital healthArticle
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10 authors.
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Abstract
objectiveThis study evaluates the accuracy of ChatGPT-4o, ChatGPT-o1, Gemini, and ERNIE Bot in answering rotator cuff injury questions and responding to patients. Results show Gemini excels in accuracy, while ChatGPT-4o performs better in patient interactions.
methodsPhase 1: Four LLM chatbots answered physician test questions on rotator cuff injuries, interacting with patients and students. Their performance was assessed for accuracy and clarity across 108 multiple-choice and 20 clinical questions. Phase 2: Twenty patients questioned the top two chatbots (ChatGPT-4o, Gemini), with responses rated for satisfaction and readability. Three physicians evaluated accuracy, usefulness, safety, and completeness using a 5-point Likert scale. Statistical analyses and plotting used IBM SPSS 29.0.1.0 and Prism 10; Friedman test compared evaluation and readability scores among chatbots with Bonferroni-corrected pairwise comparisons, Mann-Whitney U test compared ChatGPT-4o versus Gemini; statistical significance at p < 0.05.
resultsGemini achieved the highest average accuracy. In the second part, Gemini showed the highest proficiency in answering rotator cuff injury-related queries (accuracy: 4.70; completeness: 4.72; readability: 4.70; usefulness: 4.61; safety: 4.70, post hoc Dunnett test, p < 0.05). Additionally, 20 rotator cuff injury patients questioned the top two models from Phase 1 (ChatGPT-4o and Gemini). ChatGPT-4o had the highest reading difficulty score (14.22, post hoc Dunnett test, p < 0.05), suggesting a middle school reading level or above. Statistical analysis showed significant differences in patient satisfaction (4.52 vs. 3.76, p < 0.001) and readability (4.35 vs. 4.23). Orthopedic surgeons rated ChatGPT-4o higher in accuracy, completeness, readability, usefulness, and safety (all p < 0.05), outperforming Gemini in all aspects.
conclusionThe study found that LLMs, particularly ChatGPT-4o and Gemini, excelled in understanding rotator cuff injury-related knowledge and responding to patients, showing strong potential for further development.
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