Applications of Artificial Intelligence in Oral Radiology and Diagnosis: A Scoping Review

This scoping review evaluates the role of artificial intelligence, including deep learning and machine learning methods, in diagnostic applications within oral and maxillofacial radiology. The review covers panoramic and cephalometric radiographs, cone-beam computed tomography, and clinical photographs used for detecting dental caries, jaw lesions, developmental anomalies, and soft-tissue conditions. These computational models are applied for image interpretation, enhancement, and segmentation, supporting greater diagnostic consistency and precision. Most available studies are retrospective and depend on single-center datasets, limiting external validation and practical use. Differences in imaging quality, protocols, and patient characteristics further restrict reproducibility. The review identifies the need for standardized imaging datasets, transparent algorithms, and prospective clinical testing to confirm diagnostic reliability and establish safe integration into dental radiology workflows.