Artificial Intelligence in Oral and Maxillofacial Surgery: Current Applications, Methodological Challenges, and Future Directions

Abstract Artificial intelligence (AI) has emerged as a promising tool in oral and maxillofacial surgery (OMFS), particularly in enhancing diagnostic accuracy, surgical planning, and risk prediction. This narrative review critically evaluates current AI applications across key clinical domains, including lesion detection and classification in diagnostic imaging, prediction of surgical difficulty and inferior alveolar nerve injury in impacted third molar surgery, cone-beam computed tomography–based bone assessment and implant planning, automated landmark detection and surgical simulation in orthognathic surgery, classification of temporomandibular joint disorders, and prognostic modeling in oral oncology. Despite encouraging results, most studies are based on retrospective, single-center datasets with limited external or prospective validation. Methodological heterogeneity, inconsistent outcome definitions, and insufficient integration into clinical workflows remain major barriers to translation. While AI demonstrates significant potential as a decision-support tool in OMFS, current evidence does not yet support widespread clinical implementation. Future research should prioritize robust validation strategies, clinically meaningful endpoints, and seamless integration into surgical practice.