Clinical implementation of artificial intelligence in adolescent mental healthcare

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Not reported — narrative review only; no pooled effect size calculated

• Primary Outcome: Qualitative synthesis of AI tool types, clinical applications, effectiveness, safety, and ethical considerations in adolescent mental healthcare

• Nnt Or Sensitivity: Not applicable — no quantitative outcome data synthesised; effectiveness claims are qualitative and primarily limited to AI-guided therapy apps and chatbots for depression treatment

• Confidence Interval: Not reported

Clinical Application

Feasibility is context-dependent. AI-assisted clinical documentation tools may be more immediately implementable in well-resourced health systems. Patient-facing apps and chatbots require careful vetting for safety guardrails before clinical endorsement. The review explicitly cautions against recommending specific apps at this stage. Integration into clinical workflows requires staff training, governance frameworks, and ongoing monitoring. In Australia, AI-based digital mental health tools would require TGA regulatory oversight if classified as Software as a Medical Device (SaMD) under the TGA's Digital Health framework. The RACGP and Royal Australian and New Zealand College of Psychiatrists (RANZCP) have not yet issued specific guidelines on AI-assisted adolescent mental health tools. Existing digital mental health platforms (e.g., headspace, Beyond Blue, ReachOut) provide a comparator landscape. PBS does not currently subsidise AI-based mental health applications. The Australian Digital Health Agency's national digital health strategy is relevant context. Equity considerations are particularly salient given rural and remote adolescent populations with limited access to specialist mental health services, where AI tools may offer greatest benefit but also greatest risk if inadequately governed. Adolescents (approximate age range 10–24 years) presenting to mental health or general paediatric services, particularly those with depression or seeking mental health support. Clinicians working in adolescent medicine, child and adolescent psychiatry, and paediatric primary care.

Abstract

PURPOSE OF REVIEW: This review aims to summarize recent literature on artificial intelligence (AI) tools for adolescent mental health, including the types of tools available, their clinical applications, effectiveness, and safety, as well as relevant ethical considerations. RECENT FINDINGS: For clinicians, AI can facilitate clinical documentation, enhance therapy, and support the diagnosis process. Adolescents show interest in using AI for their mental healthcare and can benefit from AI-guided therapy apps and chatbots. Most studies that show effectiveness focus on depression treatment. Many tools are only in the prototyping stage, not tested on clinical samples, or lack safety measures, highlighting the need for further safety evaluation before specific app recommendations can be made. SUMMARY: AI is increasingly being implemented in pediatric health systems and adolescents' daily lives. Adolescent medicine practitioners should recognize the growing potential for certain AI applications to enhance access and support adolescents, review and utilize those applications that have empirical support of efficacy, and that provide guardrails for safe and ethical use.

References

1. 1.Bischops, A. C., Kavanaugh, J. R., & Bickham, D. S. (2026). Clinical implementation of artificial intelligence in adolescent mental healthcare. Current Opinion in Pediatrics. https://doi.org/10.1097/MOP.0000000000001584