Limitations and Pitfalls of Artificial Intelligence in Orthodontics: A Narrative Review

generalizability; (2) evaluate clinical constraints, including the inability to replicate adaptive human judgment; (3) assess ethical, legal, and regulatory challenges; and (4) outline evidence-based directions for future development and safer clinical integration of AI in orthodontics. MethodsThis study was designed as a narrative review.A comprehensive literature search was conducted across PubMed/MEDLINE, Scopus, Web of Science, Google Scholar, and the Cochrane Library from inception to December 2024.Search terms used individually and in combination included "artificial intelligence in orthodontics," "machine learning orthodontics," "deep learning orthodontics," "convolutional neural network cephalometry," "AI limitations dentistry," "automated cephalometric analysis," and "algorithmic bias dental AI." Boolean operators (AND, OR) were used to refine search combinations.Articles were included if they: (1) were published in peerreviewed journals or established academic texts; (2) addressed AI, IntroductIonArtificial intelligence (AI) has emerged as a transformative force in contemporary orthodontics, with applications spanning automated cephalometric landmark detection, treatment planning, growth assessment, and digital workflow optimization. [1][2]2][3] Machine learning and deep learning frameworks, particularly convolutional neural networks (CNNs), now enable rapid, consistent analysis of radiographic and three-dimensional imaging data, augmenting diagnostic efficiency across a range of clinical tasks. 4,5Published literature documents steady advances in model accuracy for cephalometric tracing, malocclusion classification, extraction decision-support, and orthognathic surgery planning, establishing AI as a promising adjunct in evidence-based orthodontic care. 1,2espite these advances, critical lacunae remain that limit the safe and effective translation of AI from research settings to routine clinical practice.Most AI models in orthodontics are trained on unicentric, ethnically homogeneous, or inadequately annotated datasets, compromising their generalizability across diverse patient populations. 3,4Biological individuality, including growth variability, periodontal response, and facial esthetic considerations, cannot be reliably captured by current algorithmic frameworks. 5,6Furthermore, ethical and regulatory deficiencies, including ambiguous accountability for AI-generated errors, opaque "black-box" decision-making, and the limited number of clinically validated, regulatory-approved systems, represent substantive barriers to widespread adoption. 7,8his narrative review addresses the existing gap between AI's reported research performance and its real-world clinical applicability in orthodontics.The aim of this review is to systematically examine, synthesize, and critically evaluate the current limitations, pitfalls, and barriers associated with AI implementation in orthodontic diagnosis and treatment planning.The specific objectives are to: (1) identify technical limitations related to data quality, model architecture, and