Artificial intelligence in mental health care: a scoping review of reviews
Abstract
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…
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Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
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