{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:55:48Z","timestamp":1790751348613,"version":"4.1.0"},"reference-count":65,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T00:00:00Z","timestamp":1790726400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Child Adolesc. Psychiatry"],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Mental health difficulties among adolescents and youth (ages 10\u201324) are increasing globally, yet early intervention remains impeded by stigma, service shortages, and cultural barriers. AI-enabled technologies including chatbots, automated screening tools, and machine learning-based predictive models have been proposed as scalable approaches to address these gaps, alongside non-AI digital interventions such as therapist-guided internet-delivered cognitive behavioral therapy (iCBT) and smartphone-based CBT applications, which are examined as comparator evidence given their stronger controlled-design evidence base.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This scoping review followed Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) reporting guidelines and Joanna Briggs Institute (JBI) methodology. A systematic database search across six databases (PubMed, Scopus, ProQuest, SpringerLink, Web of Science, and ScienceDirect), supplemented by manual and forward-citation searching, yielded 10,962 records, of which 19 studies met the predefined inclusion criteria. A Human-AI Collaboration Role framework comprising a four-level continuum from fully human-led to fully automated delivery was applied as a predefined coding dimension across all included studies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Most studies focused on depression and anxiety among mid-to-late adolescents in high-income countries (68.4%). Therapist-guided iCBT was the most rigorously studied format using randomized and controlled designs; AI chatbots, predictive models, and large language model (LLM)-based systems demonstrated feasibility or engagement outcomes in a limited number of studies and contexts, though randomized diagnostic chatbot trials specifically remain absent for participants under 18 years; at least one randomized non-diagnostic chatbot trial (a body-image intervention) has been conducted in this age group. Equity-related reporting was inconsistent: socioeconomic status was reported in only 10.5% of studies and cultural adaptation in 42.1%; restriction to English-language publications may further underrepresent non-Western and low- and middle-income-country evidence relevant to these equity findings. Key implementation challenges included inconsistent safeguarding protocols for minors, algorithmic bias, data privacy concerns, and insufficient integration with clinical and educational systems.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The evidence base remains preliminary and methodologically heterogeneous, insufficient to support conclusions about clinical effectiveness or generalizability to diverse adolescent populations. These findings represent a descriptive map of the current evidence landscape rather than an assessment of effectiveness. Future research should prioritize adequately powered adolescent-focused trials, participatory co-design approaches, culturally informed adaptations, and transparent ethical governance frameworks to ensure responsible development of AI-enabled digital mental health tools for youth. Because the included studies were heterogeneous and no formal risk-of-bias assessment was undertaken, this review does not establish comparative clinical effectiveness, safety, or superiority of AI-enabled interventions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Systematic Review Registration<\/jats:title>\n                    <jats:p>\n                      <jats:ext-link>https:\/\/osf.io\/sbpk2<\/jats:ext-link>\n                      , doi:\n                      <jats:ext-link>10.17605\/OSF.IO\/SBPK2<\/jats:ext-link>\n                      .\n                    <\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/frcha.2026.1905782","type":"journal-article","created":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T06:01:45Z","timestamp":1790748105000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["AI-enabled and comparator digital 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