Title : Artificial intelligence as a decision support tool for orthodontic diagnosis treatment planning and tooth movement
Abstract:
Artificial Intelligence (AI) has emerged as a transformative adjunct in contemporary orthodontics, with rapidly expanding applications in automated diagnosis, image-based analysis, treatment planning, growth assessment, and predictive modelling of orthodontic biomechanics. Conventional orthodontic workflows, although clinically effective, are limited by operator-dependent variability, time-intensive analyses, and challenges in anticipating individual biological responses to orthodontic forces. Recent advances in machine learning and deep learning have enabled AI-based systems to process complex orthodontic datasets with increasing accuracy, consistency, and efficiency. The clinical value of these technologies depends on their integration into a clinician-led, biologically driven decision-making framework.
Aim: This study aims to synthesize contemporary peer-reviewed evidence on AI applications in orthodontic diagnosis, treatment planning, growth evaluation, and outcome prediction, with a particular focus on clinical validity, biomechanical relevance, and decision-support functionality.
Materials and Methods: A comprehensive literature review was conducted using the PubMed, Scopus, and Web of Science databases, focusing on systematic and scoping reviews published between 2020 and 2025. Based on predefined inclusion criteria, a total of 20 peer-reviewed publications were analysed, addressing AI applications in cephalometric and image-based diagnostics, AI-assisted treatment-planning algorithms, skeletal maturity and growth prediction, and predictive models of orthodontic tooth movement and treatment outcomes.
Results: Automated cephalometric landmark detection remains the most extensively investigated application of artificial intelligence in orthodontics. Deep learning–based models have consistently demonstrated high diagnostic accuracy, reduced operator-dependent variability, and significant time efficiency compared with conventional manual tracing techniques. Beyond diagnostic automation, AI-supported decision systems have enabled more precise classification of malocclusion patterns, skeletal maturity stages, and craniofacial growth trajectories, supporting individualized timing of orthodontic and orthopaedic interventions.
The emerging evidence further indicates that AI-driven algorithms contribute to orthodontic treatment planning by simulating tooth movement, optimizing aligner-based therapeutic strategies, and estimating treatment duration. By integrating patient-specific anatomical, morphological, and biomechanical parameters, these models enhance treatment predictability under controlled conditions. The AI-enhanced monitoring platforms parallel facilitate continuous assessment of treatment progression, allowing earlier identification of deviations from planned biomechanics and supporting improved patient adherence.
Despite these encouraging developments, substantial heterogeneity persists across study designs, datasets, and validation methodologies. Key limitations include insufficient data standardization, restricted external validation, and incomplete modelling of individual biological variability, particularly with respect to periodontal ligament response and alveolar bone remodelling, which continue to limit widespread clinical implementation.
Conclusions: The current evidence supports the use of artificial intelligence as a valuable decision-support tool in orthodontics, offering measurable benefits in diagnostic accuracy, treatment planning consistency, and biomechanical predictability. AI systems are intended to augment, rather than replace, clinical expertise. Future research should prioritize high-quality multicentre datasets, transparent and interpretable modelling, and rigorous clinical validation to ensure the responsible integration of AI into personalized and predictable orthodontic care.
Keywords: Artificial Intelligence; Orthodontics; Decision Support Systems, Clinical; Diagnostic Imaging; Orthodontic Tooth Movement.
