Artificial intelligence in orthodontics: clinical maturity, evidence gaps, and emerging applications-a narrative review.
Authors
Affiliations (13)
Affiliations (13)
- Ziauddin University, Karachi, Pakistan.
- Dow International Dental College, Karachi, Pakistan.
- Shaheed Zulfikar Ali Bhutto Institute of Science and Technology University, Karachi, Pakistan.
- Rashid Latif Dental College, Lahore, Pakistan.
- Army Medical College, Rawalpindi, Pakistan.
- Bacha Khan Dental College, Mardan, Pakistan.
- Khyber Medical University, Peshawar, Pakistan.
- Margalla Institute of Health Sciences, Rawalpindi, Pakistan.
- Hamdan Bin Mohammed College of Dental Medicine (HBMCDM), Mohammed Bin Rashid University of Medicine and Health Sciences (MBRU), Dubai, United Arab Emirates.
- Future University in Egypt, Cairo, Egypt.
- University Hospital Schleswig-Holstein, Kiel, Germany.
- King Salman International University, El-Tor, Egypt.
- University Hospital Bonn, Bonn, Germany. [email protected].
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.