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VOLUME 2 , ISSUE 2 ( July-December, 2025 ) > List of Articles

REVIEW ARTICLE

Role of Artificial Intelligence in Modern Orthodontics: From Diagnosis to Treatment Planning, a Narrative Review

Krishi Vinod Jariwala, Ajay Kantilal Kubavat, Khyati Viralkumar Patel, Khushbu V Rathod, Vaibhavi R Bhimda

Keywords : Aligner therapy, Artificial intelligence, Cephalometrics, Deep learning, Diagnosis, Machine learning, Narrative Review, Orthodontics, Treatment planning

Citation Information : Jariwala KV, Kubavat AK, Patel KV, Rathod KV, Bhimda VR. Role of Artificial Intelligence in Modern Orthodontics: From Diagnosis to Treatment Planning, a Narrative Review. 2025; 2 (2):58-64.

DOI: 10.5005/jihr-11055-0017

License: CC BY-NC 4.0

Published Online: 30-04-2026

Copyright Statement:  Copyright © 2025; The Author(s).


Abstract

Aim and background: The integration of artificial intelligence (AI) into orthodontics has transformed diagnostic and treatment-planning workflows, driven by advances in CBCT imaging, digital scanning, cephalometric analysis, and data-driven decision support systems. In particular, machine learning (ML) and deep learning (DL) techniques are being incorporated to automate landmark identification, assist malocclusion classification, and support the prediction of orthodontic treatment outcomes. This review summarizes the current applications, benefits, and limitations of AI in orthodontic diagnosis and treatment planning. Materials and methods: A narrative review approach was adopted. The literature was searched in PubMed, Scopus, Web of Science, and Google Scholar for studies published between 2010 and 2025 using keywords including artificial intelligence, machine learning, deep learning, orthodontics, diagnosis, and treatment planning. Relevant peer-reviewed articles, clinical studies, reviews, and technical reports related to orthodontic AI applications were included. Evidence was synthesized qualitatively due to heterogeneity in study methodologies. Results: Multiple investigations have reported that AI-assisted cephalometric landmark detection achieves error values that fall within clinically acceptable limits, typically comparable to expert manual tracings. AI models also assist in extraction decision-making, anchorage prediction, aligner staging, and treatment duration forecasting. Despite promising outcomes, clinical integration remains limited due to dataset bias, transparency concerns, the requirement for validation, and ethical considerations surrounding patient data security. Conclusion: AI has significant potential to enhance precision, efficiency, and standardization in orthodontic diagnosis and treatment planning. Current evidence supports AI as a complementary tool rather than a replacement for clinical expertise. Wider adoption will require multicenter datasets, explainable algorithms, ethical frameworks, and prospective clinical validation. Clinical significance: Artificial intelligence can improve diagnostic accuracy, reduce manual tracing time, assist in treatment decision-making, and optimize aligner-based workflows. With responsible integration and continued validation, AI may become a core component of modern orthodontic practice.

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