Authors : Dr. Aishwarya Birje; Dr. Chetan Patil; Dr. Anshuj Thetay; Dr. Pradeep Kawale; Dr. Pradeep Kumar; Dr. Snehal Bhalerao; Dr. Aameer Parkar; Dr. Manorama Wakle

Volume/Issue : Volume 10 - 2025, Issue 7 - July

Google Scholar : https://tinyurl.com/39djaabm

Scribd : https://tinyurl.com/y65s6dwy

DOI : https://doi.org/10.38124/ijisrt/25jul1910

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Abstract : Artificial Intelligence (AI) is revolutionizing orthodontic practice by enhancing diagnostic precision, streamlining treatment planning, and enabling personalized patient care. Advanced AI algorithms, notably convolutional neural networks, have demonstrated high accuracy in tasks such as cephalometric landmark identification and malocclusion classification, thereby improving clinical efficiency and decision-making processes. Despite these advancements, the integration of AI into orthodontics presents several challenges. These include the need for standardized, high-quality datasets to train robust models, addressing data privacy and ethical concerns, ensuring the interpretability of AI-driven decisions, and overcoming resistance to technological adoption within clinical settings. This article provides a comprehensive review of current AI applications in orthodontics, discusses the potential benefits and limitations, and explores future directions for integrating AI technologies to enhance patient outcomes and clinical workflows.

References :

1. Nilsson NJ. Artificial Intelligence: A New Synthesis. San Francisco: Morgan Kaufmann; 1998. p. 493.
2. Goertzel B, Pennachin C. Artificial General Intelligence. Berlin, Heidelberg: Springer-Verlag; 2007.
3. Khanagar SB, Al-Ehaideb A, Vishwanathaiah S, Maganur PC, Patil S, Naik S, et al. Scope and performance of artificial intelligence technology in orthodontic diagnosis, treatment planning, and clinical decision-making: A systematic review. J Dent Sci. 2021;16(1):482–92.
4. Hung HC, Wang YC, Wu YC. Applications of artificial intelligence in orthodontics. Taiwanese J Orthod. 2020;32(2):3.
5. Mohammad-Rahimi H, Nadimi M, Rohban MH, Shamsoddin E, Lee VY, Motamedian SR, et al. Machine learning and orthodontics, current trends and the future opportunities: A scoping review. Am J Orthod Dentofacial Orthop. 2021;160(2):170–92.
6. Hussain MA, Fatima S, Reddy KK, Ramya Y, Betha SP, Kauser A, et al. Artificial intelligence in orthodontics: A review. Int J Health Sci. 2022;6(S2):9378–83.
7. Asiri SN, Tadlock LP, Schneiderman E. Applications of artificial intelligence and machine learning in orthodontics. APOS Trends Orthod. 2020;10(1):17–24.
8. Faber J, Faber C, Faber P. Artificial intelligence in orthodontics. APOS Trends Orthod. 2019;9(4):201–5.
9. Li X, Xu ZR, Tang N, Ye C, Zhu XL, Zhou T, et al. Effect of intervention using a messaging app on compliance and duration of treatment in orthodontic patients. Clin Oral Investig. 2015;20:1849–59.
10. Nanda SB, Kalha AS, Jena AK. Artificial neural network (ANN) modeling and analysis for the prediction of change in the lip curvature following extraction and non-extraction orthodontic treatment. J Dent Spec. 2015;3(2):130–9.
11. Choi E, Kim D, Lee JY, Park HK. Artificial intelligence in detecting temporomandibular joint osteoarthritis on orthopantomogram. Sci Rep. 2021;11:10246.
12. Choi HI, Jung SK, Baek SH, Lim WH, Ahn SJ, Yang IH, et al. Artificial intelligent model with neural network machine learning for the diagnosis of orthognathic surgery. J Craniofac Surg. 2019;30(7):1986–9.
13. Li P, Kong D, Tang T. Orthodontic treatment planning based on artificial neural networks. Sci Rep. 2019;9(1):1–9.
14. Bahaa K, Noor G, Yousif Y. The artificial intelligence approach for diagnosis, treatment and modelling in orthodontic. In: Principles of Contemporary Orthodontics; 2011. p. 451.
15. Xie X, Wang L, Wang A. Artificial neural network modeling for deciding if extractions are necessary prior to orthodontic treatment. Angle Orthod. 2010;80(2):262–6.
16. Jung SK, Kim TW. New approach for the diagnosis of extractions with neural network machine learning. Am J Orthod Dentofacial Orthop. 2016;149(1):127–33.
17. Patcas R, Bernini DAJ, Volokitin A, Agustsson E, Rothe R, Timofte R, et al. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg. 2019;48(1):77–83.
18. Ahmed N, Chethana, Aymen U, Rahul NA. Artificial intelligence in orthodontics: A way towards modernization. IP Indian J Orthod Dentofacial Res. 2023;9(1):3–7.
19. Schwartz WB, Patil RS, Szolovits P. Artificial intelligence in medicine: Where do we stand? N Engl J Med. 1987;316(11):685–8.
20. Hung M, Voss MW, Rosales MN, Li W, Su W, Xu J, et al. Application of machine learning for diagnostic prediction of root caries. Gerodontology. 2019;36(4):395–404.
21. Bichu YM, Hansa I, Bichu AY, Premjani P, Flores-Mir C, Vaid NR. Applications of artificial intelligence and machine learning in orthodontics: A scoping review. Prog Orthod. 2021;22(1):18.
22. Albalawi F, Khanagar SB, Iyer K, Alhazmi N, Alayyash A, Alhazmi AS, Awawdeh M, Singh OG. Evaluating the performance of artificial intelligence‑based large language models in orthodontics—A systematic review and meta‑analysis. Appl Sci. 2025;15(2):893.
23. Reddy Y, Kukalakunta Y, Thunki P. Artificial intelligence in orthodontics: Current trends and future directions. J Bioinform Artif Intell. 2024;4(1).