ArticleOrthopaedic journal of sports medicine2026
Evaluating Artificial Intelligence-Generated Responses to Patient Questions Regarding Orthobiologic Injections.
Article in Orthopaedic journal of sports medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.
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.
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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Patient interest in orthobiologic injections continues to grow. While modern patients are increasingly reliant on artificial intelligence (AI) large language models (LLMs) for health information, it remains unclear whether AI-generated responses regarding orthobiologics are both accurate and written at a reading level suitable for patient education. Purpose: To assess the accuracy and readability of responses to common patient questions regarding orthobiologic injections from 3 popular AI LLMs (ChatGPT, Gemini, and Grok). Study Design: Cross-sectional Study. Methods: Responses to 20 common patient questions regarding orthobiologic injections were recorded from ChatGPT 4o, Gemini 2.5 Flash, and Grok 3 in July 2025. Four independent reviewers (2 fellowship-trained sports medicine orthopaedic surgeons and 2 fellowship-trained nonoperative sports medicine physicians) assessed AI responses for accuracy using the ChatGPT Response Rating System (CRRS) and the AI Response Metric (AIRM). Readability of responses was assessed using the Flesch-Kincaid Grade Level (FKGL). Results: Interrater reliability was strong for all accuracy ratings (ICCs >0.70; Conclusion: In this study, ChatGPT, Gemini, and Grok provided generally accurate information on orthobiologics but failed to produce responses at a patient-appropriate readability level. Gemini outperformed ChatGPT in accuracy, although all 3 models demonstrated significant limitations in clarity. Until these issues are resolved, AI-generated responses should serve only as supplemental resources, with final patient education directed by physicians.
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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 Socratic graph.