Abstract
BACKGROUND: Artificial intelligence has been increasingly used for radiographic fracture detection in recent years. However, its performance in the diagnosis of displaced and non-displaced fractures in specific anatomical regions has not been sufficiently investigated. This study aimed to evaluate the accuracy and sensitivity of Chat Generative Pretrained Transformer (ChatGPT-4.0) in the diagnosis of radial head, distal humerus and olecranon fractures. METHODS: Anonymized radiographs, previously confirmed by an expert radiologist and orthopedist, were evaluated. Anteroposterior and lateral radiographs of 266 patients were analyzed. Each fracture site was divided into 2 groups: displaced and non-displaced. ChatGPT-4.0 asked 2 questions to indicate whether each image was broken. Responses were categorized as "fracture detected in the first question," "fracture detected in the second question," or "no fracture detected." RESULTS: ChatGPT-4.0 showed a significantly higher accuracy in diagnosing displaced fractures at all sites (P < .001). The highest fracture detection rate in the first question was observed for displaced distal humeral fractures (87.7%). The success rate was significantly lower in non-displaced fractures, and in the non-displaced group the highest diagnostic rate was observed in radial head fractures (25.3%). No statistically significant difference was found in pairwise sensitivity comparisons between non-displaced fractures (P > .05). CONCLUSION: ChatGPT-4.0 shows promising diagnostic performance in the detection of displaced olecranon, radial head and distal humeral fractures. However, its limited success in non-displaced fractures indicates that the model requires further training and development before clinical use. LEVEL OF EVIDENCE: Level 3.