What Happens when Orthodontic Image Computing Gets Smarter than We 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.