Integrating Artificial Intelligence in Orthopedic Decision-Making: A Comparative Analysis of Distal Radius Fracture Management

Abstract

Objective: Artificial intelligence (AI) is increasingly used in orthopedic decision-making, offering potential benefits in diagnostic accuracy and treatment planning. This study evaluates the alignment between AI-generated and clinician-based treatment recommendations for distal radius fractures.

Methods: A retrospective analysis was conducted on 53 patients with distal radius fractures. Radiographs were reviewed by clinicians and by the ChatGPT-4O AI model, which was provided with standardized surgical indication criteria. Artificial intelligence–generated treatment decisions (surgical vs. conservative) were compared to clinical decisions to assess concordance.

Results: Clinicians recommended surgical treatment in 9 cases (17%) and conservative treatment in 44 cases (83%). ChatGPT-4O agreed with the clinical decision in 35 cases (66%). Discrepancies occurred in 18 cases (34%), mostly involving borderline indications, where clinical context and judgment were essential.

Conclusion: While AI demonstrated reasonable alignment with expert decisions, its limitations in cases requiring nuanced interpretation were evident. The findings highlight the need for cautious integration of AI as a decision-support tool not a replacement for clinical expertise. Artificial intelligence, exemplified by ChatGPT-4O, can support orthopedic decision-making but must be used under clinical supervision. Ongoing refinement and validation are essential for safe and effective clinical integration.

Cite this article as: Celayir A, Doğan R, Davulcu CD, Aydın N. Integrating artificial intelligence in orthopedic decision-making: A comparative analysis of distal radius fracture management. Cerrahpaşa Med J 2026, 50, 0061, doi:10.5152/cjm.2026.25061.