Artificial intelligence applications in mental health care: a systematic review of current implementation, benefits, challenges, and methodology quality of empirical evidence

This systematic review synthesizes fragmented evidence on AI implementation in mental health, evaluating its effectiveness and ethical dimensions through a rigorous analysis of studies from five major databases. Utilizing the PICO framework and PRISMA guidelines, the research maps various AI methods including machine learning and natural language processing across conditions like depression and schizophrenia, while assessing methodological quality via RoB 2.0 and GRADE. The findings identify critical gaps in transparency and equity, providing a robust evidence base to inform future policy and the development of ethical safeguards in digital mental health interventions.