Year: 2024
Type: preprint
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.
Source: arXiv (Cornell University)
Authors: Jiang, Meng, Zhao, Qing, Li, Jianqiang, Wang, Fan, He, Tianyu, Cheng, Xinyan, Yang, Bing Xiang, Ho, Grace W. K., Fu, Guanghui
Institutions: Beijing University of Technology, Wuhan University, Hong Kong Polytechnic University, Centre National de la Recherche Scientifique, Institut national de recherche en sciences et technologies du numérique, Inserm, Sorbonne Université, Assistance Publique – Hôpitaux de Paris, Pitié-Salpêtrière Hospital, Institut du Cerveau
Language: English
Subfield: Applied Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W4401201708