[Paper Review] A Generic Review of Integrating Artificial Intelligence in Cognitive Behavioral Therapy

Meng Jiang, Qing Zhao+7 more|arXiv (Cornell University)|Jul 28, 2024

Digital Mental Health Interventions9 citations

TL;DR

A literature review summarizing how AI, especially pre-training models and large language models, is integrated into CBT across pre-treatment, therapy, and post-treatment stages, with discussion of datasets, benefits, and limitations.

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.

Motivation & Objective

• Survey CBT fundamentals and delivery challenges to set context for AI integration.
• Systematically categorize AI applications in CBT across pre-treatment, therapeutic process, and post-treatment stages.
• Identify datasets and data sources relevant to AI-enabled CBT tasks.
• Discuss benefits, limitations, and future research directions for AI-enhanced CBT.

Proposed method

• Perform literature search on ArXiv and Google Scholar for CBT and AI-related terms.
• Classify AI integrations by CBT delivery stage and synthesize findings.
• Highlight AI techniques used for assessment, diagnosis, emotion analysis, personalized treatment, psychoeducation, and therapy support.

Figure 1: The role of AI in various stages of CBT treatment.

Experimental results

Research questions

• RQ1 How is AI currently integrated into different stages of CBT delivery (pre-treatment, therapeutic process, post-treatment)?
• RQ2 What AI methods and datasets are used to support CBT-related tasks, and what are their limitations?
• RQ3 What are the key benefits and challenges of AI-enhanced CBT, and where should future research focus?

Key findings

• AI can augment CBT assessment, diagnosis, and cognitive distortion detection, using text, audio, and multimodal data.
• LLMs and transformer-based models are explored for cognitive restructuring, emotion analysis, and personalized treatment planning.
• Psychoeducation and CBT delivery via mobile apps, chatbots, and conversational agents are increasingly incorporating AI for accessibility and engagement.
• Personalized treatment selection and outcome prediction with AI show potential for improving resource allocation and efficacy, though evidence for long-term clinical utility remains limited.
• Current datasets for cognitive distortions and emotions are heterogeneous and limited, with challenges in short text, data imbalance, and need for cross-modal data.