Artificial intelligence in mental health care: a scoping review of reviews

AI applications span the mental-health care continuum but remain early in translation. Performance that appears strong under internal validation often attenuates on external or prospective testing; symptomatic gains are concentrated in depression/anxiety and may diminish over longer follow-up; and adoption is constrained by usability, EHR integration, and incomplete governance. The cross-review signal highlights consistent gaps in accountability, post-deployment monitoring and crisis escalation, equity reporting, workforce readiness, and life-cycle economics (including integration, monitoring, and re-training). Addressing these gaps through externally validated and monitored deployments, routine content/guardrail audits for chatbots with human escalation, predefined subgroup performance and bias auditing, and implementation strategies that pair explainability with clinician training and measure workforce endpoints would better align the evidence base with safe, effective, and sustainable clinical use.