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

IF 2.2 4区 医学 Q1 NURSING
Li-Ting Chen , Li-Ching Yang , Fang-Yi Lin , Yueh-Hsiu Lin
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引用次数: 0

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.
系统综述:人工智能驱动的认知行为疗法在自主心理健康管理中的整合
抑郁症是一种影响生活质量的广泛存在的精神健康障碍,传统的治疗方法往往需要耗费大量资源。研究表明,基于cbt的人工智能通过自主心理健康管理缓解抑郁症状的有效性。目的评价基于认知行为治疗的人工智能(AI)在抑郁症状自主健康管理中的应用效果及整合水平。方法本系统综述采用PRISMA方法学和混合方法评价工具。研究包括使用人工智能干预抑郁症的随机对照试验,分析理论框架、干预设计和结果。综述和方案被排除在外。在Cochrane图书馆、CINAHL Plus全文版和PubMed中检索2019年10月至2024年10月发表的文章。结果五项研究表明,结合认知行为治疗指导框架的人工智能设计特别表明了短期干预的有效性。在这些人工智能干预措施中,只有54%的部分整合在人工智能设计中实施了理论框架。然而,研究结果显示,参与基于人工智能的自主心理健康管理的参与者,特别是那些有中度至重度抑郁症的参与者,在强大的理论基础上,抑郁症状显著减少了60%。结论基于认知行为疗法的人工智能干预通过患者自我管理平台在减轻抑郁症状方面显示出有效性。理论驱动的方法不仅指导了人工智能应用的开发,还促进了自动化心理健康干预措施的实施,从而减少了护理人员的工作量。cbt引导的人工智能技术的整合使患者能够使用自我管理工具,同时优化精神卫生机构的护理资源。
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来源期刊
CiteScore
3.70
自引率
0.00%
发文量
131
审稿时长
160 days
期刊介绍: Archives of Psychiatric Nursing disseminates original, peer-reviewed research that is of interest to psychiatric and mental health care nurses. The field is considered in its broadest perspective, including theory, practice and research applications related to all ages, special populations, settings, and interdisciplinary collaborations in both the public and private sectors. Through critical study, expositions, and review of practice, Archives of Psychiatric Nursing is a medium for clinical scholarship to provide theoretical linkages among diverse areas of practice.
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