Artificial intelligence-enabled mental health applications for adolescents: a scoping review of safety, effectiveness and clinical implementation.

OBJECTIVES

To map artificial intelligence (AI)-enabled mental health applications for adolescents and distinguish the available evidence on clinical functions, predictive or diagnostic performance, symptom-level effectiveness, safety, governance and implementation readiness.

DESIGN

Scoping review conducted using Joanna Briggs Institute guidance and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) extension for Scoping Reviews.

DATA SOURCES: PubMed/MEDLINE, Scopus, the Cochrane Library and ScienceDirect were searched from 1 January 2014 to 17 June 2026.

ELIGIBILITY CRITERIA: English-language peer-reviewed sources addressing AI-enabled screening, prediction, assessment, monitoring, therapeutic support, care navigation, safeguarding or implementation for adolescents or school-aged youth were eligible. Adult-only studies, non-AI digital tools, protocols without results and unverifiable or promotional sources were excluded.

CHARTING AND SYNTHESIS: Primary studies and evidence syntheses were analysed separately. 95 primary studies were mapped using mutually exclusive principal AI-modality and clinical-function categories; effectiveness, safety and implementation domains were non-mutually exclusive.

RESULTS

The database searches identified 11 167 records, and 1 additional evidence synthesis was identified and assessed during revision; 103 sources were included, comprising 95 primary studies and 8 evidence syntheses. Structured-data machine learning was the dominant modality (42/95, 44.2%), and risk prediction or prognosis was the leading clinical function (45/95, 47.4%). Predictive or diagnostic performance was reported in 83 studies (87.4%), but symptom-level effectiveness in only 1 (1.1%). No primary study provided a dedicated prospective AI-safety evaluation. Implementation evidence focused on bias or equity monitoring (28/95, 29.5%), privacy or data governance (26/95, 27.4%), human oversight (19/95, 20.0%), acceptability (18/95, 18.9%) and feasibility (15/95, 15.8%). No study evaluated sustained routine-care scale-up or implementation cost as a principal outcome.

CONCLUSIONS

Within the retrieved evidence base, adolescent mental-health AI research was concentrated in technical development, with limited prospective evidence of clinical benefit, safety and routine implementation. The field is dominated by prediction and internal validation, whereas symptom-level benefit, prospective safety, workflow integration, scale-up and sustainability remain largely untested. Future applications should be evaluated as supervised, human-in-the-loop systems with explicit safeguards for consent, privacy, fairness, accountability and crisis escalation.