Advancements in Dental Science through Artificial Intelligence and Machine Learning: A Comprehensive Review

This narrative review examines the application of artificial intelligence (AI) and machine learning (ML) across three major learning paradigms-supervised, unsupervised, and reinforcement learning (RL). The former has shown remarkable accuracy in diagnosing dental diseases, such as caries, periodontal conditions, and oral cancers; the second uses unsupervised learning techniques, such as clustering and association rule mining, to uncover hidden patterns in patient data, improving preventive care and resource allocation; and the latter uses RL techniques, such as Q-learning, deep Q-networks (DQNs), and actor-critic models, to support adaptive and personalized treatment strategies by continuously optimizing decisions based on patient feedback. From AI-driven imaging tools to algorithms encouraging oral self-care behaviors and other dental treatments, this review highlights significant developments and real-world applications. To overcome the obstacles and realize the full potential of AI and ML in dentistry, future studies and interdisciplinary cooperation will be crucial. By adopting these technologies, the dental industry can transform the delivery of oral health care and move toward a more patient-centered, data-driven, and efficient model of treatment.

Sharma A, Agarwal N. Advancements in Dental Science through Artificial Intelligence and Machine Learning: A Comprehensive Review. Int J Clin Pediatr Dent 2026;19(1):120-126.