Deep Learning

Artificial intelligence (AI) is increasingly influencing modern orthodontic practice by facilitating more precise diagnosis, individualized treatment planning, and improved prediction of treatment outcomes. This review explores the clinical applications of AI in orthodontics, focusing on the contributions of machine learning and deep learning to contemporary diagnostic and therapeutic procedures. AI-driven technologies enable automated interpretation of radiographs, facial photographs, and digital dental models, supporting cephalometric analysis, cervical vertebral maturation assessment, and anatomical landmark identification. Their applications also encompass extraction decision-making, bracket positioning, fixed appliance therapy, clear aligner treatment, and orthognathic surgical planning. In clear aligner therapy, AI facilitates virtual treatment simulation, prediction of tooth movement, optimization of aligner staging, digital fabrication, and remote monitoring of treatment progress. Furthermore, predictive algorithms may assist in evaluating treatment complexity and anticipating postoperative soft tissue changes. Despite these advances, clinical implementation remains constrained by limitations in training data quality, algorithmic bias, generalizability, data security, regulatory frameworks, and implementation costs. Establishing the reliability of AI-based systems requires standardized assessment protocols and robust validation across diverse patient populations. Consequently, AI should currently be integrated as an adjunct to, rather than a substitute for, the clinical judgment and expertise of orthodontists. With continued technological development, rigorous clinical evaluation, and appropriate ethical safeguards, AI has the potential to strengthen evidence-based decision-making and advance efficient, predictable, and patient-centered orthodontic care.