GD564 - What every dentist needs to know about the use of artificial intelligence in orthodontic treatment planning-GD564 Article

GD564 Article

Pdf Summary

Artificial intelligence (AI) is increasingly being used to support orthodontic diagnosis, treatment planning, appliance design, and patient monitoring. Machine-learning and deep-learning systems can analyze radiographs and cone-beam CT scans, identify cephalometric landmarks, detect malocclusions and other abnormalities, and help predict treatment needs and outcomes. Some studies report high accuracy, and automated cephalometric analysis may be substantially faster than manual methods, although clinician review remains important.<br /><br />AI can also help customize brackets and clear aligners, simulate tooth movement and treatment outcomes, and monitor progress using patient images, scanners, wearables, or remote-monitoring applications. Alerts may flag poor appliance fit, missed treatment steps, or unexpected tooth movement, allowing earlier intervention and potentially fewer routine office visits. Visual simulations and real-time feedback may improve communication, patient understanding, and adherence. Remote tools may also expand access to orthodontic care, particularly for underserved or rural communities.<br /><br />These benefits come with significant limitations. AI predictions depend on the quality and representativeness of training data; biased datasets can produce inaccurate results and worsen disparities. Systems require validation, staff training, and integration into existing clinical workflows. Ethical issues, transparency, and professional oversight must also be addressed.<br /><br />The article emphasizes that AI should complement—not replace—orthodontists. Clinical expertise, judgment, and understanding of individual patient needs and preferences remain essential. Responsible adoption requires careful dataset development, ongoing validation, awareness of bias, and human review of AI-supported recommendations.

Keywords

artificial intelligence

orthodontics

machine learning

deep learning

cephalometric analysis

radiographs

cone-beam CT

treatment planning

remote monitoring

bias and validation