AI-enhanced orthodontic treatment planning - A scoping review on Evidence-based clinical application with commercial software overview.

Abstract

Objective: This scoping review aimed to identify evidence-based research papers on AI-enhanced treatment planning tools for orthodontics. The clinical relevance and applicability of academically validated AI tools were examined and complemented by an assessment of commercially available software.

Data sources: PubMed, Embase, Web of Science, and Cochrane were searched up to February 2025 for English-language studies on AI-based orthodontic treatment planning tools.

Study selection: Included studies were validation, accuracy, evaluation research papers; were available in full text; published in English; and focused on digital orthodontic treatment planning.

Results: Of 307 studies identified, 17 met inclusion criteria. The most focused on AI-driven decision-making for orthodontic extractions and design. Others explored automation for deep-bite planning and expansion in mixed dentition. Several studies evaluated large language models (LLMs) for answering orthodontic questions. Included were eight evaluation studies, three validation studies, two accuracy studies, and three comparative studies. AI methods included machine learning, deep learning, and LLMs, with reported accuracies from 72% to 95%. Seven commercial AI tools for orthodontic treatment planning were identified.

Conclusions: The reviewed studies primarily addressed key treatment planning decisions or broader treatment recommendations. Academically validated tools typically rely on clinician-provided text inputs, whereas commercial AI solutions can process raw clinical data, such as intraoral scans. There is a mismatch between academically validated tools and commercially available systems, which generally lack published validation. This gap highlights the need for validation of commercial tools to ensure effective clinical integration.

Clinical significance: AI-based tools for orthodontic treatment planning might enhance clinical efficiency and promote consistency in decision-making. Both academic and commercial solutions demonstrate significant potential as decision-support systems, reinforcing - rather than replacing - the expertise of orthodontic professionals.