ArticleJournal of orthopaedic case reports2026
Performance of ChatGPT-5 in Diagnosing Fractures on Proximal Humerus and Intertrochanteric Femur X-Rays.
Article in Journal of orthopaedic case reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Large language models (LLMs) such as ChatGPT-5 offer new possibilities for interpreting medical images, but their effectiveness in orthopedic radiograph analysis remains largely unexplored. Objective: To evaluate the diagnostic performance of ChatGPT-5 in detecting and classifying fractures on shoulder and hip X-rays, specifically proximal humerus and intertrochanteric (IT) femur fractures. Materials and Methods: A retrospective study of 120 anonymized anteroposterior (AP) radiographs (60 shoulder and 60 hip) was conducted. Each case was independently reviewed by orthopedic experts, establishing a reference standard. ChatGPT-5 analyzed the same images using structured prompts and was assessed for fracture detection accuracy, sensitivity, specificity, and agreement on detailed fracture features. Results: ChatGPT-5 achieved 87.5% sensitivity and 100% specificity in detecting proximal humerus fractures (κ = 0.74), and 100% sensitivity but only 16.7% specificity in IT femur fractures (κ = 0.24). While it identified major fracture patterns and comminution reliably, it frequently hallucinated fractures in normal hip X-rays and missed fine details such as lesser tuberosity fragments and dislocations. Conclusion: ChatGPT-5 shows high sensitivity for orthopedic fracture detection and produces coherent, structured reports. However, limitations in specificity and fine-detail recognition restrict its autonomous clinical use. It may serve as a triage or educational tool with human oversight or be integrated into hybrid artificial intelligence workflows.
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