How Can Research on Artificial Empathy Be Enhanced by Applying Deepfakes?

Area of Science:

• Medical Informatics
• Computer Vision
• Artificial Intelligence

Background:

• Facial emotion recognition (FER) aids empathic care but is limited by data scarcity and privacy concerns in medical settings.
• Acquiring large, diverse datasets for FER in clinical contexts is challenging due to patient privacy regulations and data sharing restrictions.
• Existing face recognition datasets are often small, hindering the development of robust artificial empathy models.

Purpose of the Study:

• To propose a novel method for generating de-identified patient video data using deepfake technology for research.
• To facilitate the development of artificial empathy systems by creating an open dataset of doctor-patient interactions.
• To enhance the application of facial emotion recognition in clinical practice to improve patient care and therapeutic relationships.

Main Methods:

• Utilizing deepfake technology to replace original patient faces in video recordings with synthetic, anonymized faces while preserving emotional expressions.
• Developing an open dataset of doctor-patient interactions suitable for training facial emotion recognition algorithms.
• Leveraging facial emotion recognition to enable doctors to better understand and respond to patient emotions.

Main Results:

• Deepfake technology offers a viable solution for de-identifying sensitive patient video data, overcoming privacy barriers.
• The proposed method enables the creation of larger, more accessible datasets for training artificial empathy models.
• Facial emotion recognition, enhanced by synthetic data, can significantly improve the delivery of empathic care.

Conclusions:

• Deepfake-generated datasets hold significant potential for advancing artificial empathy in healthcare.
• This approach can revolutionize the use of facial emotion recognition to foster better doctor-patient relationships and therapeutic alliances.
• Improved empathic care through artificial empathy may lead to increased patient satisfaction and treatment adherence.