Artificial Intelligence in Mental Health: A Structured Review of Current Evidence across the Care Pathway

Abstract

: Artificial Intelligence (AI) is increasingly deployed across the mental health pathway, from screening and diagnosis through to intervention, monitoring and prognosis. This paper presents a structured review, aligned with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidance, of the current evidence base for AI in mental health. Working from four anchor reviews and a transparent, criteria-driven corpus of supporting primary and regulatory sources, we make four contributions. First, we propose a faceted taxonomy that classifies any mental-health AI system across paradigm, data modality, clinical task, autonomy/risk and evidence maturity. Second, we report an explicit search protocol with inclusion and exclusion criteria, so that the evidence base is reproducible rather than implicit. Third, we synthesise reported performance comparatively across application domains and grade the maturity of the evidence using a five-level scheme. Fourth, we propose a Responsible AI Pipeline that connects research activity to safe clinical deployment through explicit bias, validation, safety and regulatory gates. Reported strengths include accurate classification and risk prediction for common mental disorders, earlier case-finding, scalable chatbot-based self-help, and support for personalised treatment planning. However, the literature remains marked by methodological inconsistency, limited external validation, bias in training data, under-representation of people with intellectual disability and other marginalised groups, and unresolved issues around consent, explainability and regulation. We argue that AI should be framed as an augmentation of - not a replacement for - the clinical relationship, with equity, consent and explainability treated as first-order design constraints.