Artificial Intelligence in Dentistry: Integrated Applications and Specialty-Specific Advances
Abstract
Artificial intelligence (AI) is transitioning from a supportive tool to a core component of clinical decision-making in dentistry. This review synthesizes advances in deep learning and machine learning for oral disease diagnosis, orthodontic treatment prediction, pediatric oral health management, adolescent psychological-oral health interactions, and precision care for special-needs populations, drawing on evidence published predominantly between 2024 and 2026. A systematic search across PubMed, Web of Science, and Scopus identified 30 studies for narrative synthesis. Several AI models reported higher diagnostic or predictive performance than selected conventional comparators in detecting periodontal bone loss, temporomandibular joint osteoarthritis, and oral squamous cell carcinoma, with accuracy frequently exceeding 90%. In orthodontics, AI-driven prediction reduces tooth movement error rates substantially. However, most studies rely on single-center retrospective data lacking prospective multicenter validation. Future priorities include multimodal data fusion, federated learning, and standardized validation frameworks to facilitate clinical translation.
