ArticleCureus2025
Do ChatGPT and Gemini Provide Appropriate Recommendations for Anterior Cruciate Ligament and Isolated Meniscal Injuries?
Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Do ChatGPT and Gemini provide accurate answers to patient questions about glenohumeral arthritis?JSES reviews, reports, and techniques · 2026Article
- Artificial intelligence and machine learning in sports medicine: mapping clinical tasks and assessing clinical maturity - a scoping review.BMC medical informatics and decision making · 2026Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and purposeThe ability of large language models (LLMs) such as ChatGPT and Gemini to respond accurately to sports surgery-related patient questions remains unknown. This study aimed to compare the responses of ChatGPT and Gemini regarding anterior cruciate ligament (ACL) and meniscal injuries with the American Academy of Orthopaedic Surgeons (AAOS) Evidence-Based Clinical Practice Guidelines (CPGs) recommendations.
methodsWe queried ChatGPT and Gemini with questions based on statements from the AAOS CPGs for ACL and meniscus injuries. Responses were classified by two reviewers as "Agree," "Neutral," or "Disagree" with the AAOS CPGs. A Cohen's kappa coefficient was used to assess interrater reliability, and chi-squared analyses were used to compare responses between LLMs.
resultsOf the 11 CPG recommendations that were of strong or moderate strength, ChatGPT and Gemini provided responses that were in agreement for 9 (82%) and 8 (73%) of recommendations, respectively. While both LLMs showed perfect concordance with the meniscus CPGs, there were no significant differences when comparing the ACL responses or when comparing the LLMs' responses to strong and moderate recommendation queries. ChatGPT provided zero study references, whereas Gemini provided 25 PubMed resources, of which 23 appropriately supported the claims made in the Gemini responses.
conclusionsWhile there is still room for growth and transparency in these large language models, providers can expect that these AI platforms generally provide information to patients that aligns with lower extremity sports surgery clinical practice guidelines.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.