Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications.

IF 5.9 2区医学 Q1 PSYCHIATRY Psychological Medicine Pub Date : 2025-02-06 DOI:10.1017/S0033291724003295

Pablo Cruz-Gonzalez, Aaron Wan-Jia He, Elly PoPo Lam, Ingrid Man Ching Ng, Mandy Wingman Li, Rangchun Hou, Jackie Ngai-Man Chan, Yuvraj Sahni, Nestor Vinas Guasch, Tiev Miller, Benson Wui-Man Lau, Dalinda Isabel Sánchez Vidaña

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引用次数: 0

Abstract

Artificial intelligence (AI) has been recently applied to different mental health illnesses and healthcare domains. This systematic review presents the application of AI in mental health in the domains of diagnosis, monitoring, and intervention. A database search (CCTR, CINAHL, PsycINFO, PubMed, and Scopus) was conducted from inception to February 2024, and a total of 85 relevant studies were included according to preestablished inclusion criteria. The AI methods most frequently used were support vector machine and random forest for diagnosis, machine learning for monitoring, and AI chatbot for intervention. AI tools appeared to be accurate in detecting, classifying, and predicting the risk of mental health conditions as well as predicting treatment response and monitoring the ongoing prognosis of mental health disorders. Future directions should focus on developing more diverse and robust datasets and on enhancing the transparency and interpretability of AI models to improve clinical practice.

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来源期刊

Psychological Medicine 医学-精神病学

CiteScore

11.30

自引率

4.30%

发文量

711

审稿时长

3-6 weeks

期刊介绍： Now in its fifth decade of publication, Psychological Medicine is a leading international journal in the fields of psychiatry, related aspects of psychology and basic sciences. From 2014, there are 16 issues a year, each featuring original articles reporting key research being undertaken worldwide, together with shorter editorials by distinguished scholars and an important book review section. The journal''s success is clearly demonstrated by a consistently high impact factor.