Artificial Intelligence in Oral & Maxillofacial Radiology: Evidence, Clinical and Future Directions

Artificial intelligence (AI) has moved from proof of concept to clinical deployment across multiple domains of oral and maxillofacial radiology (OMR). Deep learning systems now assist with detection and quantification of dental caries and periodontal bone loss on ۲D radiographs; automate cephalometric landmarking; and support lesion detection, segmentation, and risk stratification on cone beam CT (CBCT). In parallel, AI driven radiomics applied to CT/MRI/PET for head and neck oncology shows promise for nodal metastasis prediction and outcome modeling. Regulatory cleared dental AI systems are in active clinical use, and controlled studies indicate improvements in diagnostic sensitivity and decision making particularly for less experienced readers while highlighting persistent challenges around generalizability, bias, and case complexity. This narrative review synthesizes recent evidence (۲۰۲۴–۲۰۲۵), pragmatic integration strategies, validation requirements, and ethical/regulatory considerations, and outlines near term research priorities for trustworthy, equitable, and explainable OMR AI.

کلیدواژه ها:

artificial intelligence ، deep learning ، dentomaxillofacial radiology ، CBCT ، cephalometric analysis ، caries detection ، radiomics ، head and neck cancer ، explainability ، FDA clearance