AI-Driven Approaches for Enhancing Early Detection of Dental Caries

Dental caries is a widespread oral health concern that requires early detection and prevention to minimize long-term complications. Despite progress in AI-driven diagnostics, challenges such as poor image quality, limited model generalizability, and lack of interpretability hinder clinical adoption. This review explores recent advances in applying artificial intelligence (AI) to dental caries detection and prevention, including deep learning, transfer learning, and self-supervised techniques. Methods like U-Net, ResNet, and ensemble CNNs have shown effectiveness in classification, segmentation, and progression prediction, achieving accuracies of 85–99%, precisions of 86–96%, and F1-scores of 87–94%. In prevention, AI has enabled risk assessment and personalized early intervention strategies. A novel framework that integrates digital twins with Generative AI, Reinforcement Learning, and Explainable AI for real-time, personalized diagnostics is proposed to address current limitations. This work lays the foundation for a comprehensive, interpretable, and adaptive AI-based dental caries management system that can transform future oral healthcare practices.