In recent years, artificial intelligence has ceased to be a promise for the future and has become a tool for everyday clinical use in multiple medical specialties. orthodontics This is no exception. The enormous amount of data generated by orthodontic practice (cephalometric images, digitized plaster models, intraoral records, facial and volumetric photographs) constitutes a particularly fertile ground for training machine learning algorithms and convolutional neural networks.
The adoption of AI in orthodontics This approach responds not only to a technological trend, but also to specific clinical needs: reducing inter- and intra-observer variability in diagnosis, shortening planning times, and ultimately improving patient outcomes. In this article, we analyze the three areas where the scientific evidence is already sufficiently robust to draw relevant clinical conclusions.
1. Automated cephalometric analysis
He cephalometric tracing Manual cephalometric analysis is one of the most common (and most error-prone) procedures in orthodontics. Classic studies show that intraobserver variability in the location of cephalometric points can reach several millimeters, with direct implications for diagnosis and treatment planning.
Current AI systems, trained on tens of thousands of teleradiographs labeled by specialists, are capable of identify cephalometric points with accuracy comparable to or better than that of an expert orthodontist. A systematic review with meta-analysis published in Clinical Oral Investigations (Schwendicke et al., 2021) He analyzed studies on automated point localization in 2D and 3D radiographs and concluded that Deep learning algorithms achieve an accuracy comparable to that of an expert operator, with average errors below the clinically relevant threshold in most of the evaluated points. Analysis time is also significantly reduced compared to conventional manual tracing.
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Beyond point location, some systems integrate the automatic interpretation of the analysis (skeletal classification, vertical pattern assessment, identification of asymmetries), generating preliminary reports that the clinician can review and edit. This does not replace the specialist's judgment, but it does free up cognitive time for more complex decision-making.
2. AI in planning with digital aligners
The rise of thermoformed aligners has generated an unprecedented digital infrastructure: millions of cases with initial models, proposed treatment plans, and actual results. This massive database is precisely the fuel AI needs to learn how to predict which movements are biologically viable, in how many steps, and with what probability of success.
The leading manufacturers of aligners (Align Technology with Invisalign, among others) have been integrating for years predictive algorithms on their planning platforms. The iTero Element system, for example, uses AI to generate a real-time result simulation during the intraoral scan, which also has added value in communication with the patient.
From a scientific point of view, a study of Lafer et al. published in the Journal of Clinical Medicine (2024) They applied machine learning models to a sample of nearly 10,000 patients to predict the need for refinements in aligner treatment. The results showed that AI models are able to meaningfully identify cases with a higher risk of requiring intermediate adjustments., This opens the door to more precise planning from the outset. These figures point to a scenario in which AI acts as a systematic second opinion tool, reducing planning errors that are currently only identified midway through treatment.
3. Prediction of craniofacial growth
One of the most complex questions in orthodontics is how much and how will a growing patient grow?. The response determines whether functional orthotics should be used, when to initiate active treatment, and what the long-term outcome will be. Traditionally, this prediction has been based on indicators of skeletal maturation. (cervical vertebrae stages, Björk stage) with limited reliability.
AI models trained with skeletal maturation images have shown promising results compared to classical methods in predicting the pubertal growth spurt. Kunz et al. (Journal of Imaging, 2024) They developed a model based on convolutional neural networks which combines cervical vertebral maturation and second molar calcification to estimate the skeletal maturation stage from orthopantomograms. The model obtained a discriminative capacity superior to conventional visual methods, with significantly shorter processing times.
These tools are especially promising in the context of early diagnosis, where the therapeutic window is critical and the accuracy of the prognosis conditions decisions of great importance for the patient's development.
Clinical considerations and current limitations
The Adoption of AI in orthodontic clinical practice It must be done with a critical eye. Most available studies present Important methodological limitations: training samples biased towards certain ethnicities or facial types, validation in single centers, and poor generalizability to populations other than the training population.
In addition, Current AI lacks the ability to integrate the contextual information that clinicians naturally synthesize: The patient's attitude, expectations, medical history, periodontal conditions, and anticipated level of cooperation are all factors to consider. In this sense, the most appropriate model is not that of AI as a substitute for the orthodontist, but rather as clinical decision support tool, which expands the professional's capabilities without replacing their judgment.
From an ethical and regulatory point of view, it should be emphasized that in the European Union AI systems with medical applications are subject to European Medical Devices Regulation (MDR 2017/745) and, from 2024, to the Artificial Intelligence Act (AI Act), which classifies AI-assisted diagnostic systems as high-risk and requires rigorous clinical validation before they can be marketed.
Conclusion
Artificial intelligence has moved beyond the experimental phase in orthodontics and is beginning to be truly integrated into the clinical workflow. Automated cephalometric analysis, aligner-assisted planning, and growth prediction are the areas with the strongest available evidence and the greatest potential for short-term impact.
The immediate challenge for orthodontists is not so much learning to program algorithms as developing the critical thinking skills to evaluate, select, and correctly interpret the AI tools available on the market. Continuing education in this area is no longer optional; it has become a fundamental clinical competency.
At Koline Institute we closely follow these advances to rigorously and patient-centeredly integrate them into our daily practice.
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