Artificial intelligence meets pediatric orthopedics: A comparative analysis of ChatGPT-4o, Gemini 2.0, and Claude 3.5 in detecting supracondylar humeral fractures

14 Jul 20264 min read0 viewsJournal Feed

GIST (Key Takeaways)

by Utku Murat Kalafat, Hüseyin Mutlu, Ramiz Yazıcı, Murat Genç, Bensu Bulut, Medine Akkan Öz, Ayşenur Gür, Mehmet Yortanlı, Uğur Şakar Background Supracondylar humeral fractures constitute 10–16% of pediatric skeletal injuries, requiring timely diagnosis to prevent neurovascular complications. Developmental variations in pediatric bone structures pose diagnostic challenges for clinicians.
This study evaluated three next-generation large language models (LLMs) (ChatGPT-4o, Gemini 2.0, Claude 3.5) for detecting pediatric supracondylar humeral fractures and their classification according to the Gartland system. Methods This retrospective observational study included 300 pediatric patients (150 with supracondylar humeral fractures confirmed by expert consensus, 150 without fractures) aged 2–10 years presenting to the Emergency Department of the Bilkent City Hospital (October 2022-January 2025).
Two-view elbow radiographs were presented to each LLM three times on different days. Diagnostic accuracy was evaluated using overall accuracy (all three responses correct), strict accuracy (≥2 correct responses), and ideal accuracy (≥1 correct response).
Response consistency was assessed using Fleiss’ Kappa coefficient. Fractures were classified according to modified Gartland criteria.

Clinical Editorial

PLOS ONE (Medicine) published a clinical update in Research Highlights on 14 Jul 2026.

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