Decoding wisdom: Evaluating ChatGPT's accuracy and reproducibility in analyzing orthopantomographic images for third molar assessment

Abstract

The integration of Artificial Intelligence (AI) into healthcare has opened new avenues for clinical decision support, particularly in radiology. The aim of this study was to evaluate the accuracy and reproducibility of ChatGPT-4o in the radiographic image interpretation of orthopantomograms (OPGs) for assessment of lower third molars, simulating real patient requests for tooth extraction. Thirty OPGs were analyzed, each paired with a standardized prompt submitted to ChatGPT-4o, generating 900 responses (30 per radiograph). Two oral surgery experts independently evaluated the responses using a three-point Likert scale (correct, partially correct/incomplete, incorrect), with disagreements resolved by a third expert. ChatGPT-4o achieved an accuracy rate of 38.44 % (95 % CI: 35.27 %-41.62 %). The percentage agreement among repeated responses was 82.7 %, indicating high consistency, though Gwet's coefficient of agreement (60.4 %) suggested only moderate repeatability. While the model correctly identified general features in some cases, it frequently provided incomplete or fabricated information, particularly in complex radiographs involving overlapping structures or underdeveloped roots. These findings highlight ChatGPT-4o's current limitations in dental radiographic interpretation. Although it demonstrated some capability in analyzing OPGs, its accuracy and reliability remain insufficient for unsupervised clinical use. Professional oversight is essential to prevent diagnostic errors. Further refinement and specialized training of AI models are needed to enhance their performance and ensure safe integration into dental practice, especially in patient-facing applications.

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