Artificial intelligence in mental health care: a systematic review of diagnosis, monitoring, and intervention applications
Pablo Cruz-Gonzalez, Anxun He, Eva K. M. Lam, Irene Ai Ting Ng, Mingze Li, Rangchun Hou, Jackie Ngai-Man Chan, Yuvraj Sahni, Nestor Viñas‐Guasch, Tiev Miller, Benson Wui-Man Lau, Dalinda Isabel Sánchez-Vidaña
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.