Systematic review: The integration of artificial intelligence-powered cognitive-behavioural therapy for autonomous mental health management

Li-Ting Chen; Li-Ching Yang; Fang-Yi Lin; Yueh-Hsiu Lin

doi:10.1016/j.apnu.2025.151916

Systematic review: The integration of artificial intelligence-powered cognitive-behavioural therapy for autonomous mental health management

Arch Psychiatr Nurs. 2025 Aug:57:151916. doi: 10.1016/j.apnu.2025.151916. Epub 2025 Jun 19.

Authors

Li-Ting Chen¹, Li-Ching Yang², Fang-Yi Lin³, Yueh-Hsiu Lin⁴

Affiliations

¹ Department of Nursing, College of Nursing, Fooyin University, Kaohsiung, Taiwan, ROC. Electronic address: AA292@fy.edu.tw.
² Department of Nursing, College of Nursing, Fooyin University, Kaohsiung, Taiwan, ROC. Electronic address: ft079@msg.fy.edu.tw.
³ Department of Psychology, College of Humanities and Social Science, Kaohsiung Medical University, Taiwan. Electronic address: wellsugars@gmail.com.
⁴ School of Nursing, College of Nursing, Kaohsiung Medical University, Kaohsiung, Taiwan, ROC; Department of Medical Research, Kaohsiung Medical University Hospital, Taiwan. Electronic address: yhlin@kmu.edu.tw.

PMID: 40816782
DOI: 10.1016/j.apnu.2025.151916

Abstract

Background: Depression is a widespread mental health disorder that affects quality of life, with traditional treatments often resource-intensive. Studies have demonstrated the effectiveness of CBT-based AI in alleviating depressive symptoms through autonomous mental health management.

Purpose: To evaluate the effect and the integration level of Cognitive-Behavioural Therapy-based artificial intelligence (AI) on autonomous health management in depressive symptoms care.

Methods: This systematic review used the PRISMA methodology and a mixed-methods appraisal tool. Studies included randomized controlled trials using artificial intelligence interventions for depression, analysing theoretical frameworks, intervention designs, and outcomes. Reviews and protocols were excluded. Data sources were searched in the Cochrane Library, CINAHL Plus with Full Text, and PubMed for articles published between October 2019 and October 2024.

Results: Five studies demonstrated that artificial intelligence designs incorporating the Cognitive-Behavioural Therapy guidance framework specifically indicated short-term intervention effectiveness. Of these AI interventions, only partial integration of 54 % implemented a theoretical framework in AI design. Nevertheless, findings revealed a significant 60 % decrease in depressive symptoms among participants who engaged with the AI-based autonomous mental health management, particularly those with moderate-to-severe depression, when grounded in a strong theoretical foundation.

Conclusion: Cognitive-Behavioural Therapy-based artificial intelligence interventions have demonstrated effectiveness in decrease depressive symptoms through patient self-management platforms. The theory-driven approach not only guides the development of AI applications but also facilitates the implementation of automated mental health interventions, thereby reducing the workload of nursing staff. This integration of CBT-guided AI technology empowers patients with self-management tools while optimizing nursing resources in mental health settings.

Keywords: Artificial intelligence; College students; Depressive symptoms; Systematic review; Young adults.

Publication types

Systematic Review

MeSH terms

Artificial Intelligence*
Cognitive Behavioral Therapy* / methods
Depression* / therapy
Humans