Artificial Intelligence in Mental Health Care: A Scoping Review of Reviews

Mohammad S. Abu-MahfouzSarah AlFehaidHala M. BurqanRabie Adel El Arab^*

• Almoosa College of Health Sciences, Al Ahsaa, Saudi Arabia

Background Artificial intelligence (AI) is rapidly entering mental health care, but most models remain proof-of-concept, with limited external validation and substantial risk of overfitting. Methods This scoping review of reviews adhered to the PRISMA-ScR checklist and Joanna Briggs Institute guidance. We searched MEDLINE, Embase, PsycINFO, and IEEE Xplore. Eligible publications encompassed systematic, scoping, narrative, integrative, meta-analytic, and patent reviews. Findings were synthesised thematically. Results Thirty-one reviews were included. Evidence concentrated on depression and anxiety; schizophrenia, bipolar disorder, perinatal mental health, autism spectrum conditions, older adults, nurses, and allied professionals were under-represented. Across screening, diagnosis/classification, and risk prediction, high accuracy was frequently reported under internal validation; in prior syntheses, typical internal AUCs clustered around ≈0.80–0.88 whereas externally or prospectively validated performance was scarce and typically attenuated. Signals were strongest for narrow, feedback-rich tasks, with greater decay for general-purpose models and longer prediction horizons. Conversational agents produced small-to-moderate short-term improvements in depressive symptoms (SMD ≈0.2–0.6); effects for anxiety and stress were smaller or inconsistent and varied with comparator stringency, follow-up (≤8–12 weeks vs longer), and the degree of human guidance. Most chatbot evaluations were short and small-scale, with few randomized or pragmatic trials and limited data on durability beyond 12 weeks. Real-world implementation was limited; several reviews identified usability and electronic health-record integration as prerequisites for adoption, and explainability alone rarely conferred actionability without clinician training. Ethical readiness was incomplete: privacy and bias were commonly discussed, but accountability, post-deployment monitoring, and crisis-escalation protocols were inconsistently specified. Economic evaluations were uncommon and rarely accounted for integration, maintenance, or re-training costs. Workforce outcomes (literacy, confidence, readiness) were infrequently measured. Internal and external metrics were not pooled. Conclusions 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).