Tehran University of Medical Sciences Journal of Craniomaxillofacial Research 2345-5489 11 4 2024 12 31 215 222 EN Ali Mirzaei Student Research Committee, Faculty of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran. Melika Mollaei Student Research Committee, Faculty of Dentistry, Mazandaran University of Medical Sciences, Sari, Iran. Anahita Lotfizadeh Student Research Committee, Faculty of Dentistry, Mazandaran University of Medical Sciences, Sari, Iran. Mehdi Aryana Student Research Committee, Faculty of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran. Alireza Ebrahimpour Department of Oral and Maxillofacial Surgery, Student Research Committee, Faculty of Dentistry, Mashhad University of Medical Sciences, Mashhad, Iran. 2025 02 10 2025 03 04 Introduction: Artificial Intelligence (AI) is transforming dental practice through its ability to analyze, learn, and support clinical decisions. By enhancing diagnostic precision and treatment planning, AI tools are revolutionizing patient care. Despite its potential, implementation faces obstacles including ethical considerations and algorithmic limitations. This review examines AI applications across dental specialties. Materials and Methods: This narrative review was conducted by analyzing recent studies on AI applications in dentistry. The literature was sourced from reputable databases, including PubMed and Scopus, focusing on AI-driven diagnostic and therapeutic advancements in oral and maxillofacial surgery, radiology, restorative dentistry, orthodontics, periodontics, endodontics, prosthodontics, and forensic dentistry. Results: AI demonstrates remarkable capabilities across dental fields. Deep learning systems excel in detecting caries, periapical lesions, and fractures through radiological analysis. Orthodontic applications include automated cephalometric analysis and treatment simulation. In restorative dentistry, AI enhances cavity detection and restoration assessment. Maxillofacial applications include surgical outcome prediction and pathology identification. Forensic applications facilitate age and gender determination through radiographic analysis. Current challenges include data security, algorithmic bias, and ethical compliance. Conclusion: While AI shows promise in advancing dental diagnostics and treatment accuracy, successful clinical integration requires addressing privacy concerns, establishing regulatory standards, and developing comprehensive professional training programs. Keywords: Artificial intelligence; Dentistry; Diagnostic imaging; Machine learning; Digital dentistry. https://jcr.tums.ac.ir/index.php/jcr/article/view/528 https://jcr.tums.ac.ir/index.php/jcr/article/download/528/429