Orthodontists

Examine, diagnose, and treat dental malocclusions and oral cavity anomalies. Design and fabricate appliances to realign teeth and jaws to produce and maintain normal function and to improve appearance.

The occupation "Orthodontists" has an estimated automation risk of 35.8%, which is slightly below the base risk of 36.4%. This moderate risk is due to a mix of highly procedural tasks that could be automated and higher-level, person-oriented duties that require human expertise. With advancements in artificial intelligence and imaging technologies, certain aspects of the orthodontic workflow—particularly diagnostic analyses and pattern recognition—are increasingly susceptible to automation. Tasks involving data analysis and structured decision-making processes are especially vulnerable because AI systems can efficiently handle large volumes of diagnostic data and identify abnormalities with a high degree of accuracy. Among the most automatable tasks for orthodontists are: diagnosing teeth and jaw or other dental-facial abnormalities; examining patients to assess abnormalities of jaw development, tooth position, and other dental-facial structures; and studying diagnostic records, such as medical or dental histories, plaster models, photos, and X-rays, to develop treatment plans. These duties tend to be routine, protocol-driven, and dependent on interpreting visual and historical data—areas where machine learning and computer vision have made significant inroads. As technologies like digital imaging, pattern recognition, and automated reporting continue to evolve, these processes may increasingly shift from human professionals to AI-supported platforms, streamlining early diagnostics and planning. On the other hand, the most automation-resistant tasks include designing and fabricating custom orthodontic appliances, coordinating orthodontic services with other healthcare providers, and instructing dental officers and technical assistants in procedures and techniques. These responsibilities demand significant original thinking and adaptability, as they involve hands-on fabrication, interprofessional collaboration, and educational expertise. Bottleneck skills, particularly originality (scored at 3.0% and 3.3%), present notable challenges for automation since AI currently struggles to match human creativity and interpersonal nuance in complex, one-of-a-kind cases and teaching settings. As a result, while routine diagnostic and analytical tasks may become increasingly automated, the personalized, creative, and collaborative aspects of orthodontics remain highly resilient to technological replacement.