Related Experiment Video
Updated: Oct 11, 2026

Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
Published on: February 23, 2024
Artificial intelligence in orthodontics: clinical maturity, evidence gaps, and emerging applications-a narrative
Kanza Chandio1, Laiba Iftikhar2, Anum Shehzad3
1Ziauddin University, Karachi, Pakistan.
Abstract:
Artificial intelligence (AI) is rapidly transforming orthodontics by enhancing diagnostic accuracy, supporting clinical decision-making, and facilitating digital workflows. This narrative review provides a comprehensive overview of current AI technologies and their applications in orthodontics, including diagnostic imaging, treatment planning, treatment monitoring, patient communication, education, and emerging generative AI tools. The available evidence indicates that automated cephalometric landmark detection and cone-beam computed tomography (CBCT) segmentation have among the most advanced technical and external-validation evidence bases in orthodontic AI, whereas digital-model analysis has demonstrated high technical performance but more limited independent validation. However, no application met the predefined criteria for higher clinical maturity because prospective clinical effectiveness remained insufficiently established. Other emerging or experimental applications include AI-assisted treatment planning, remote monitoring, large language models (LLMs), and robotic systems, for which evidence remains predominantly retrospective, technical, feasibility-based, or early clinical. However, much of the current evidence is derived from retrospective studies, technical validation investigations, and proof-of-concept reports, with limited prospective clinical data demonstrating improvements in treatment quality, efficiency, or long-term patient outcomes. Important challenges remain regarding data quality, algorithmic bias, external validation, explainability, patient privacy, and regulatory oversight. Overall, AI should currently be regarded as a clinician-supervised decision-support tool rather than a replacement for professional expertise. Future progress is likely to depend on robust prospective clinical trials, transparent reporting, and responsible integration of AI into evidence-based orthodontic practice and the broader digital transformation of dentistry.
