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About this paper
Meng Jiang, Qing Zhao, Jianqiang Li, Fan Wang, Tianyu He, Xinyan Cheng, Bing Xiang Yang, Grace W. K. Ho and 1 more
This paper provides a detailed review of how artificial intelligence is being integrated into various stages of Cognitive Behavioral Therapy, from assessment to post-treatment, highlighting its potential to enhance accessibility, personalization, and efficiency in mental healthcare delivery. The review notes the promise of Large Language Models in specific therapeutic tasks and identifies key challenges such as data scarcity and the need for robust clinical validation.
Key insights
- Artificial intelligence offers a comprehensive toolkit to augment and optimize Cognitive Behavioral Therapy across its entire lifecycle, from initial assessment to long-term follow-up.
- Large Language Models (LLMs) demonstrate particular promise in tasks such as identifying cognitive distortions, facilitating cognitive restructuring, and delivering personalized psychoeducation.
- Despite advancements, challenges remain, including the scarcity of high-quality, diverse datasets, issues with data imbalance and labeling reliability, and the need for rigorous clinical validation of AI-enhanced CBT solutions.
Summary by alphaXiv.
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