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May 6, 2026Arthroscopy Sports Medicine and RehabilitationOpen Access

ChatGPT, Claude Sonnet, and Grok Display Similarly Low Rates of Accuracy in Identifying Image‐Based Orthopaedic Sports Pathologies

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Authors

AAAhmad R. AlhankawiCBCollin BraithwaiteAHAlejandro M Holle

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Overview

Assessment reveals low accuracy rates for ChatGPT, Grok, and Claude Sonnet in identifying sports-related pathologies, suggesting limitations in AI diagnostic capabilities.

Key Points

  • The study aimed to evaluate artificial intelligence's ability to identify common sports-related pathologies through radiologic images.
  • Comparative analysis of ChatGPT 4.0, Grok 2, and Claude 3.5 Sonnet
  • Assessment of five common orthopaedic sports pathologies
  • Collection of 50 images for each pathology from a radiologic imaging database
  • Calculation of receiver operator characteristic curves and area under the curve values
  • ChatGPT accurately identified 23.6% of diseased images, Grok 15.7%, and Claude Sonnet 17.1%
  • Most accurate detection for meniscus pathologies was 48% for ChatGPT and 42% for Grok
  • Area under the curve values were 0.21 for ChatGPT, 0.16 for Grok, 0.15 for Claude Sonnet, indicating poor accuracy

Cite This Study

Alhankawi et al. (2026) studied this question.

synapsesocial.com/papers/69fa986a04f884e66b53226chttps://doi.org/10.1002/ars2.70009
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Also Consider

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