The Integration of Artificial Intelligence in Orthodontic Diagnosis and Treatment Planning: A PRISMA-Guided Systematic Review

Background Artificial intelligence (AI) applications in orthodontics are rapidly expanding across diagnosis, image analysis, and treatment planning. Methods A PRISMA-guided systematic review was conducted. PubMed/MEDLINE, Scopus, Web of Science, and Google Scholar were searched from 2010 to 16 September 2025. Original studies in orthodontics that used AI or machine learning for diagnosis, prediction, image analysis, or treatment planning were eligible. Two reviewers independently screened records, extracted data, and assessed risk of bias using QUADAS-2 for diagnostic accuracy studies and PROBAST for prediction model studies. Owing to heterogeneity in study design, datasets, and outcome metrics, results were synthesized narratively. Results Of 1,162 records identified, 1,008 remained after duplicate removal and were screened by title and abstract. A total of 154 full-text articles were assessed for eligibility, and 45 met the inclusion criteria. Frequent AI tasks included cephalometric landmark detection, malocclusion classification, extraction-decision support, treatment duration prediction, and cone-beam computed tomography (CBCT)-based segmentation. Many studies reported high accuracies for cephalometric landmark detection (mean radial error < 2 mm and successful detection rates > 80%) and malocclusion classification (accuracies > 85%). However, risk-of-bias concerns, particularly in analysis and validation domains, were common, and external validation was infrequent. Conclusions AI models show promising performance for orthodontic diagnosis and treatment planning and may enhance efficiency and standardization of care. Nevertheless, non-standardized outcome measures, limited external validation, and insufficient reporting of model development and evaluation currently restrict clinical translation. Larger, multicenter datasets, standardized benchmarks, and robust validation—ideally following AI-specific reporting guidelines—are required before routine clinical adoption. Registration PROSPERO CRD420251134644.