Development of Facial Recognition in Clinical Decision Support

Abstract

This chapter discusses the current state of the art of facial recognition technology for clinical decision support through a systematic literature review that identifies the medical areas where it is used, the source of facial datasets used, the machine learning approach and algorithms used, and how it has been the evaluated. Findings show that the technology has been used in diagnosing genetic disorders, mental illness, and depression, and to train the algorithms, the studies identified used either publicly available datasets, published datasets from the literature, or they collected their own data. However, the majority of papers did not explicitly explain how these datasets were obtained. The finding also shows CNN is currently the most used ML algorithm for facial recognition while appearance-based is the most common approach. The evaluations in the shortlisted papers generally focus on the accuracy of the facial recognition capabilities, and the empirical evidence shows the advantage of the technology in supporting clinicians to diagnose symptoms through facial expressions.