Artificial Intelligence in Orthodontics: From Algorithms to Aligners artificial-intelligence-in-orthodontics-from-algorithms-to-aligners Volume 6, Issue 1 ARTICLES 2026 6 Artificial intelligence (AI) has become an important component of modern orthodontics, significantly influencing diagnostics, treatment planning, and patient monitoring. This review paper examines the current applications of artificial intelligence (AI) and digital technologies in orthodontic practice, with particular focus on cephalometric analysis, clear aligner therapy, predictive tooth movement, and digital treatment workflows. Relevant scientific literature was retrieved from PubMed, Scopus, Web of Science, and Google Scholar, focusing on machine learning (ML), deep learning (DL), cone-beam computed tomography (CBCT), intraoral scanning, and artificial intelligence (AI)-assisted orthodontic software systems. The reviewed studies demonstrate that AI improves diagnostic accuracy, accelerates cephalometric analysis, enhances treatment simulations, and supports individualized orthodontic care. AI-assisted aligner systems and remote monitoring technologies also improve treatment efficiency, patient compliance, and communication. Despite these advantages, orthodontic treatment remains dependent on biological variability and clinical expertise. Artificial intelligence (AI) should therefore be regarded as a supportive tool rather than a replacement for the clinician. The future of orthodontics will likely depend on the balanced integration of advanced digital technologies and professional clinical judgment.

]]> Ahn, H. J., Byun, S. H., Baek, S. H., Park, S. Y., Yi, S. M., Park, I. Y., On, S. W., Kim, J. C., & Yang, B. E. (2024). A comparative analysis of artificial intelligence and manual methods for three-dimensional anatomical landmark identification in dentofacial treatment planning. Bioengineering, 11(4), Article 318. https://doi.org/10.3390/bioengineering11040318

 

Alhazmi, A. S. (2025). Advancements in orthodontic aligner fabrication: A comparative review of 3D printing technologies and traditional methods evaluation. Journal of Contemporary Dental Practice, 26(6), 632–637.

 

Cao, L., He, H., & Hua, F. (2022). Deep learning algorithms have high accuracy for automated landmark detection on 2D lateral cephalograms. Journal of Evidence-Based Dental Practice, 22(4), Article 101798. https://doi.org/10.1016/j.jebdp.2022.101798

 

Fawaz, P., El Sayegh, P., & Vande Vannet, B. (2023). What is the current state of artificial intelligence applications in dentistry and orthodontics? Journal of Stomatology, Oral and Maxillofacial Surgery, 124(5), 101524. https://doi.org/10.1016/j.jormas.2023.101524

Khijmatgar, S., Tumedei, M., Del Fabbro, M., & Tartaglia, G. M. (2022). Effectiveness and efficacy of thermoformed and 3D-printed aligners in correcting malocclusion and their impact on periodontal health and oral microbiome: A double-blinded parallel randomized controlled multicenter clinical trial. Microorganisms, 10(7), Article 1452. https://doi.org/10.3390/microorganisms10071452

 

Kavasoglu, N., Ertugrul, O. F., Kotan, S., Hazar, Y., & Eratilla, V. (2025). Artificial intelligence-assisted wrist radiography analysis in orthodontics: Classification of maturation stage. Applied Sciences, 15, Article 11681. https://doi.org/10.3390/app152111681

 

Lee, H., Cho, J. M., Ryu, S., Ryu, S., Chang, E., Jung, Y.-S., & Kim, J.-Y. (2023). Automatic identification of posteroanterior cephalometric landmarks using a novel deep learning algorithm: A comparative study with human experts. Scientific Reports, 13, Article 15506. https://doi. org/10.1038/s41598-023-42870-z

 

Liu, J., Zhang, C., & Shan, Z. (2023). Application of artificial intelligence in orthodontics: Current state and future perspectives. Healthcare, 11(20), Article 2760. https://doi.org/10.3390/healthcare11202760

 

Nordblom, N. F., Büttner, M., & Schwendicke, F. (2024). Artificial intelligence in orthodontics: Critical review. Journal of Dental Research, 103(6), 577–584. https://doi. org/10.1177/00220345241235606

 

Song, C., Jeong, Y., Huh, H., Park, J.-W., Paeng, J.-Y., Ahn, J., Son, J., & Jung, E. (2024). Multi-scale 3D cephalometric landmark detection based on direct regression with 3D CNN architectures. Diagnostics, 14(22), Article 2605. https://doi.org/10.3390/diagnostics14222605

 

Turner, E. R. (2025). Digital workflow in orthodontics: Enhancing accuracy and treatment outcomes. International Journal of Orthopedic and Orthodontic Research, 1(3), 11–13.

 

Ye, Y., Pandey, A., Bawden, C., Sumsuzzman, D. M., Rajput, R., Shoukat, A., Singer, B. H., Moghadas, S. M., & Galvani, A. P. (2025). Integrating artificial intelligence with mechanistic epidemiological modeling: A scoping review of opportunities and challenges. Nature Communications, 16, Article 581. https://doi.org/10.1038/s41467-024-55461-x

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