ArticleArthroscopy, sports medicine, and rehabilitation2025
Microsoft Copilot Provides More Accurate and Reliable Information About Anterior Cruciate Ligament Injury and Repair Than ChatGPT and Google Gemini; However, No Resource Was Overall the Best.
Article in Arthroscopy, sports medicine, and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Ability of Large Language Models to Answer Patients' Questions and Generate Educational Materials for Uncommon Retinal Conditions.Journal of vitreoretinal diseases · 2026Article
- Can large language models be trusted? Reliability and readability of responses to perinatal depression FAQs.Frontiers in public health · 2026Article
- Comparison of the information of generative artificial intelligence large language models and professional guidelines regarding nutritional advice for orthodontic patients.BMC oral health · 2025Article
- Artificial Intelligence Can Answer Postoperative Questions About Distal Radius Fractures-But Can Patients Understand the Answers?Journal of hand surgery global online · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: To analyze and compare the quality, accuracy, and readability of information regarding anterior cruciate ligament (ACL) injury and reconstruction provided by various artificial intelligence AI interfaces (Google Gemini, Microsoft Copilot, and OpenAI ChatGPT). Methods: Twenty questions regarding ACL reconstruction were inputted into ChatGPT 3.5, Gemini, and the more precise subinterface within Copilot and were categorized on the basis of the Rothwell criteria into Fact, Policy, and Value. The answers generated were analyzed using the DISCERN scale, JAMA benchmark criteria, and Flesch-Kincaid Reading Ease Score and Grade Level. The citations provided by Gemini and Copilot were further categorized by source of citation. Results: All 3 AI interfaces generated DISCERN scores (≥50) demonstrating "good" quality of information except for Policy and Value by Copilot which were scored as "excellent" (≥70). The information provided by Copilot demonstrated greater reliability, with a JAMA benchmark criterion of 3 (of 4) as compared with Gemini (1) and ChatGPT (0). In terms of readability, the Flesch-Kincaid Reading Ease Score scores of all 3 sources were <30, apart from Fact by Copilot (31.9) demonstrating very complex answers. Similarly, all Flesch-Kincaid Grade Level scores were >13, indicating a minimum readability level of college level or college graduate. Finally, both Copilot and Gemini had a majority of references provided by journals (65.6% by Gemini and 75.4% by Copilot), followed by academic sources, whereas Copilot provided a greater number of overall citations (163) as compared with Gemini (64). Conclusions: Microsoft Copilot was a better resource for patients to learn about ACL injuries and reconstruction compared with Google Gemini or OpenAI ChatGPT in terms of quality of information, reliability, and readability. The answers provided by LLMs are highly complex and no resource was overall the best. Clinical Relevance: As artificial intelligence models continually evolve and demonstrate increased potential for answering complex surgical questions, it is important to investigate the quality and usefulness of the responses for patients. Although these resources may be helpful, they should not be used as a substitute for any discussions with health care providers.
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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.