Implications of AI-Driven Emotion Recognition Systems for Clinical Psychology

Abstract

In the current paper, the development and therapeutic implications of AI-based emotion detection systems are discussed with regards to their use in clinical psychology.  The study used a mixed-method approach of experimentation based on a quantitative machine learning framework and qualitative clinical methods to develop a unified model of multimodal emotion detection.  Preprocessing and analyzing facial expression, speech and physiological response data were done using convolutional neural networks (CNNs) and Bi-directional Long Short-Term Memory (Bi-LSTM) networks. This gave high classification accuracy in six major emotional categories.  Evaluations by clinical experts were compared to model outputs and a strong concordance index was obtained, hence confirming reliability and clinical significance.  The congruence of AI-based predictions with psychological ones was also supported by the qualitative theme analysis and indicated that the system could be used to track the treatment process and early diagnose emotional disorders.  The appropriate implementation of AI technologies in healthcare was supported by such ethical practices as anonymization and the compliance with the HIPAA and GDPR.  The results show that the emotion identification with AI can be improved significantly to enhance the accuracy of a diagnosis and personalization of treatment in clinical psychology, and it is imperative to continue enhancing AI to reduce bias and manage contextual heterogeneity.