We propose the idea of using an open data set of doctor-patient interactions to develop artificial empathy based on facial emotion recognition. Facial emotion recognition allows a doctor to analyze patients' emotions, so that they can reach out to their patients through empathic care. However, face recognition data sets are often difficult to acquire; many researchers struggle with small samples of face recognition data sets. Further, sharing medical images or videos has not been possible, as this approach may violate patient privacy. The use of deepfake technology is a promising approach to deidentifying video recordings of patients' clinical encounters. Such technology can revolutionize the implementation of facial emotion recognition by replacing a patient's face in an image or video with an unrecognizable face-one with a facial expression that is similar to that of the original. This technology will further enhance the potential use of artificial empathy in helping doctors provide empathic care to achieve good doctor-patient therapeutic relationships, and this may result in better patient satisfaction and adherence to treatment.
Keywords: artificial empathy; artificial intelligence; deepfakes; doctor-patient relationship; face emotion recognition; facial emotion recognition; facial recognition; medical images; patient; physician; therapy.
©Hsuan-Chia Yang, Annisa Ristya Rahmanti, Chih-Wei Huang, Yu-Chuan Jack Li. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 04.03.2022.