Clinical Efficacy, Therapeutic Mechanisms, and Implementation Features of Cognitive Behavioral Therapy–Based Chatbots for Depression and Anxiety: Narrative Review

Abstract

Background: Cognitive behavioral therapy (CBT)-based chatbots, many of which incorporate artificial intelligence (AI) techniques, such as natural language processing and machine learning, are increasingly evaluated as scalable solutions for addressing mental health issues, such as depression and anxiety. These fully automated or minimally supported interventions offer novel pathways for psychological support, especially for individuals with limited access to traditional therapy. Objective: This narrative review synthesized evidence on the clinical efficacy, therapeutic mechanisms, and technological features of CBT-based chatbots designed to alleviate depressive and anxiety symptoms. Methods: Fourteen peer-reviewed studies published between January 2015 and March 2025 were identified through systematic searches and met predefined inclusion criteria. The studies were analyzed to extract information on intervention structure, therapeutic components, outcomes, and implementation characteristics. Results: Across the included studies, CBT-based chatbots consistently demonstrated short-term reductions in depressive symptoms, whereas findings for anxiety outcomes were mixed, with some studies reporting improvements and others showing nonsignificant or unreported effects. Moderate effect sizes were observed for depression. Reported therapeutic features included cognitive restructuring, behavioral activation, relaxation and mindfulness strategies, emotional support, self-monitoring and feedback, and therapeutic alliance. Technological characteristics such as real-time feedback and adaptive goal tracking were associated with enhanced engagement and adherence. Conclusions: CBT-based chatbots appear to be a promising and scalable modality for delivering psychological support, particularly for underserved populations. However, variability in study designs, heterogeneity of outcome reporting, and limited long-term evidence pose challenges for generalizability. Emerging evidence from generative AI chatbots (eg, Therabot and Limbic Care) highlights both opportunities and risks. Future work should examine long-term efficacy, adaptive personalization, cross-cultural adaptation, and rigorous ethical oversight.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 20 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.