This study aims to evaluate the diagnostic reliability of the multimodal artificial intelligence model ChatGPT-4o in classifying acetabular fractures using the Letournel-Judet classification system. The study retrospectively analyzed standardized pelvic radiographs (anteroposterior, iliac oblique, and obturator oblique) from 184 patients presenting with pelvic injuries. The diagnostic performance of ChatGPT-4o was compared against the independent assessments of two fourth-year orthopaedic residents and a reference standard established by an experienced trauma surgeon using multiplanar computed tomography (CT) and intraoperative findings. By utilizing a systematic radiographic checklist, the study assesses the AI (artificial intelligence) model's ability to identify key anatomical landmarks and integrate them into a final fracture pattern. This research aims to provide critical data on the current feasibility of using large language models as decision-support tools in complex orthopaedic trauma.