A Generic Review of Integrating Artificial Intelligence in Cognitive Behavioral Therapy
Meng Jiang, Qing Zhao, Jianqiang Li, Fan Wang, Tianyu He, Xinyan Cheng, Bing Xiang Yang, Grace W. K. Ho, Guanghui Fu
TL;DR
This review surveys how AI, including pre-trained language models and large language models, can augment CBT across pre-treatment, therapeutic process, and post-treatment stages. It synthesizes evidence on diagnostic support, distortion detection, emotion analysis, personalized treatment selection, and AI-assisted delivery tools, with examples spanning cognitive restructuring, behavioral activation, exposure therapy, homework, monitoring, and outcome prediction. The paper also catalogs public datasets relevant to CBT tasks, discusses benefits and current limitations (e.g., multimodal data, standardization, and long-term efficacy), and outlines directions for future research toward accessible, efficient, and personalized AI-augmented CBT. It emphasizes responsible deployment that complements rather than replaces human therapists, aiming to improve global mental health care delivery.
Abstract
Cognitive Behavioral Therapy (CBT) is a well-established intervention for mitigating psychological issues by modifying maladaptive cognitive and behavioral patterns. However, delivery of CBT is often constrained by resource limitations and barriers to access. Advancements in artificial intelligence (AI) have provided technical support for the digital transformation of CBT. Particularly, the emergence of pre-training models (PTMs) and large language models (LLMs) holds immense potential to support, augment, optimize and automate CBT delivery. This paper reviews the literature on integrating AI into CBT interventions. We begin with an overview of CBT. Then, we introduce the integration of AI into CBT across various stages: pre-treatment, therapeutic process, and post-treatment. Next, we summarized the datasets relevant to some CBT-related tasks. Finally, we discuss the benefits and current limitations of applying AI to CBT. We suggest key areas for future research, highlighting the need for further exploration and validation of the long-term efficacy and clinical utility of AI-enhanced CBT. The transformative potential of AI in reshaping the practice of CBT heralds a new era of more accessible, efficient, and personalized mental health interventions.