Machine learning is a rapidly growing subset of artificial intelligence (AI) which involves computer algorithms that automatically build mathematical models based on sample data. Systems can be taught to learn from patterns in existing data in order to make similar conclusions from new data. The use of AI in facial emotion recognition (FER) has become an area of increasing interest for providers who wish to quantify facial emotion before and after interventions such as facial reanimation surgery. While FER deep learning algorithms are less subjective when compared to layperson assessments, the databases used to train them can greatly alter their outputs. There are currently many well-established modalities for assessing facial paralysis, but there is also increasing interest in a more objective and universal measurement system to allow for consistent assessments between practitioners. The purpose of this article is to review the development of AI, examine its existing uses in facial paralysis assessment, and discuss the future directions of its implications.
