Comparing the Effectiveness of Artificial Intelligence Technology with 6th Year Dental Students for the Diagnosis of Inflammatory Bone Lesions of the Mandible in Panoramic Radiography.

This study aimed to evaluate the potential role of artificial intelligence (AI) as a diagnostic support tool for inexperienced clinicians by comparing its diagnostic performance and time efficiency with those of sixth-year dental students in detecting mandibular inflammatory bone lesions on panoramic radiographs. A total of 412 radiographs, taken between 2013 and 2023 from the Faculty of Dentistry, Khon Kaen University, were retrospectively collected and categorized into lesion-present (n = 192) and lesion-free (n = 220) groups, including osteomyelitis (OM), radiation-induced osteomyelitis, osteoradionecrosis, and medication-related osteonecrosis of the jaw. All images were annotated by an oral and maxillofacial radiologist and surgeon using the Roboflow platform. A You Only Look Once version 8 (YOLOv8)-based deep learning detection model was developed and evaluated using an independent test set. In parallel, 20 sixth-year dental students assessed a standardized test set of 10 panoramic radiographs (6 lesion-present and 4 lesion-free images), with diagnostic accuracy and interpretation time recorded. On an independent test set (n = 62), the AI model achieved an accuracy of 97.18% with 94.87% sensitivity and 100% specificity. When evaluated on the same standardized 10-image test set used for student comparison, the model demonstrated 90% accuracy with 100% sensitivity and 75% specificity, whereas the students achieved a mean accuracy of 29.5% (sensitivity 33.33%, specificity 23.75%; P < 0.001). The AI model also required significantly less interpretation time (0.274 s) than the students (274.05 s, P < 0.001). These findings suggest that AI demonstrates strong diagnostic capability and substantial time efficiency in detecting mandibular inflammatory bone lesions on panoramic radiographs and may serve as a valuable supportive tool to enhance diagnostic accuracy, particularly in reducing missed lesions, among less experienced clinicians.