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

Abstract

Abstract 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.