TY - JOUR
T1 - Artificial intelligence in mental health care
T2 - A systematic review of diagnosis, monitoring, and intervention applications
AU - Cruz-Gonzalez, Pablo
AU - He, Aaron Wan Jia
AU - Lam, Elly Po Po
AU - Ng, Ingrid Man Ching
AU - Li, Mandy Wingman
AU - Hou, Rangchun
AU - Chan, Jackie Ngai Man
AU - Sahni, Yuvraj
AU - Vinas Guasch, Nestor
AU - Miller, Tiev
AU - Lau, Benson Wui Man
AU - Sánchez Vidaña, Dalinda Isabel
N1 - Publisher Copyright:
© The Author(s), 2025.
PY - 2025/2/6
Y1 - 2025/2/6
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - chatbot
KW - machine learning
KW - mental health
UR - http://www.scopus.com/inward/record.url?scp=85217577465&partnerID=8YFLogxK
U2 - 10.1017/S0033291724003295
DO - 10.1017/S0033291724003295
M3 - Review article
AN - SCOPUS:85217577465
SN - 0033-2917
VL - 55
JO - Psychological Medicine
JF - Psychological Medicine
M1 - e18
ER -