The accreditors of this session require that you periodically check in to verify that you are still attentive.
Please click the button below to indicate that you are.
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:
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.