AI in orthodontic treatment planning: a systematic review comparing learning approaches

Artificial intelligence has increasingly been applied to orthodontic diagnosis and treatment planning. Clinical decision-making in orthodontics is complex and often varies among practitioners, particularly for decisions such as tooth extraction, skeletal classification, and selection of treatment modality. This systematic review aimed to evaluate how artificial intelligence and knowledge-based systems have been developed and assessed for orthodontic treatment planning, to compare their performance with expert clinicians, and to examine how different learning approaches influence accuracy, interpretability, and potential clinical integration.

A comprehensive search of PubMed, Embase, Scopus, Web of Science, Cochrane Library, and IEEE Xplore was conducted from database inception to 15 October 2025 without language or date restrictions. Eligible studies evaluated artificial intelligence-based or knowledge-based systems using real orthodontic patient data to support diagnosis or treatment planning. Two reviewers independently screened records, extracted data, and assessed risk of bias using the ROBINS-I tool. Due to heterogeneity in study designs, decision tasks, and outcome measures, findings were synthesized narratively rather than through meta-analysis.

Nineteen studies met inclusion criteria. Most investigated supervised machine-learning models for extraction decisions, skeletal classification, or multi-step treatment planning, while others evaluated rule-based, fuzzy, Bayesian, or hybrid systems. Reported accuracies frequently exceeded 80%, and in some studies surpassed 90% when compared with expert decisions. Knowledge-based systems, including rule-based, fuzzy, and Bayesian/probabilistic approaches, offered greater transparency in reasoning, whereas data-driven models often demonstrated higher discriminative performance. Overall risk of bias was predominantly moderate to serious, largely due to retrospective designs, single-center samples, limited external validation, and reliance on expert opinion as the reference standard.

Artificial intelligence systems can approximate expert orthodontic decision-making for defined diagnostic and planning tasks and show promise as clinical decision-support tools. However, current evidence is limited by methodological weaknesses and lack of prospective validation. These systems should presently be considered adjunctive aids rather than autonomous planners. Robust multicenter studies with external validation are needed before routine clinical implementation can be recommended.

PROSPERO 1025581. Registered prospectively.