Artificial Intelligence Applications in Personalized Cognitive Behavioral Therapy

Abstract

This research is a mixed-method experimental investigation into the ways in which Artificial Intelligence (AI) can enhance Personalized Cognitive Behavioural Therapy (AI-CBT).  A reinforcement learning framework to dynamically adjust CBT sessions to individual patient characteristics was proposed, supported by multimodal psychometric and natural language processing of therapy transcripts.  A randomized controlled trial was carried out (N=120) in three groups: psychoeducational controls, therapist-led CBT, and AI-CBT.  The quantitative results indicated that during the baseline, mid-intervention, and post-intervention, participants in the AI-CBT group significantly minimized the symptoms of anxiety and depression measured with the BDI-II and GAD-7 (p < 0.05).  Effect size estimates confirmed the high level of AI-motivated personalization impact, and the consistency of therapeutic outcomes was confirmed with repeated measures ANOVA.  The presence of positive patient experiences with the involvement, accessibility, and personalization were highlighted through the provision of the complementary qualitative analysis, however, the concerns with the lack of human empathy were also brought up.  The thematic insights and the quantitative clinical measures served to enhance the validity of the results and proved AI-CBT to be a successful and scalable intervention.  Altogether, the present study provides empirical evidence that AI can transform the method by which therapy is provided by making the interventions context-aware, data-driven, and flexible. This will assist in addressing the growing demand of mental health solutions globally that are readily available.  The conclusion of the study addresses implications of human-AI hybrid therapeutic models in the future, as well as ethical issues surrounding digital health, in terms of its acceptance by clinicians.