AI applications in orthodontic care aren’t limited to diagnosing tooth malalignment and treatment simulations. The integration of information of multiple sources of diagnostic records (digital dental models, patients photographs, x-rays and Cone-Beam Computed Tomography) is needed to determine what the machine learning algorithms need to analyze to properly learn how to diagnose a patient’s malocclusion. Current AI algorithms often have errors that are not smarter than we are but can aid our clinical decision making in a time efficient manner. The information that the AI is absorbing comes from a number of factors from digital dental models, patients photographs, x-rays and Cone-Beam Computed Tomography.
Learning Objectives:
After this lecture, attendees will be able to:
Recognize what AI applications to incorporate in your practice.
Evaluate real versus virtual dental, skeletal and soft tissue outcomes for personalized treatment.
Define AI challenges in data quality and case-specific learning.
