Application and effectiveness of artificial intelligence in adolescent mental health support and therapy: a scoping review

AI Summary

AI accurately identifies and predicts adolescent mental health risks, offers personalised, accessible support, and enhances clinical and self-management for depression, anxiety, suicidal ideation.
Persistent technical and ethical challenges include algorithmic errors, reliance on self-reported data, complex interfaces, data security, privacy, limited transparency, and consent deficiencies.
Further real-world validation, expanded evidence, stronger regulatory frameworks, and improved digital competencies among healthcare providers are required for sustainable therapeutic effects.

BMC Psychiatry. 2026 May 23. doi: 10.1186/s12888-026-08190-y. Online ahead of print.

ABSTRACT

BACKGROUND: Adolescents are highly vulnerable to mental health conditions, yet traditional counseling models may not fully address their evolving needs. The rise of artificial intelligence (AI) has created new opportunities and challenges for supporting adolescent mental health.

OBJECTIVE: This scoping review evaluates the current applications, outcomes, strengths, drawbacks, and technical and ethical challenges of AI in adolescent mental health.

METHODS: The review adopted the PCC framework and was conducted in accordance with JBI guidelines, with reporting following the PRISMA-ScR framework. A systematic search was performed across PubMed, Embase, Web of Science, and ScienceDirect for peer-reviewed studies and grey literature published in English between 2015 and July 24, 2025. Title, abstract, and full-text screenings were independently conducted by two reviewers, with disagreements resolved by consensus or consultation with a third reviewer.

RESULTS: Of the screened records, 24 studies met the eligibility criteria, all of which were published between 2020 and 2025. USA and China were the leading contributors. AI was primarily applied to address depression, anxiety, stress, suicidal ideation, and other mental health conditions. AI demonstrated promise in accurate risk prediction, enhanced clinical and self management, personalized care, expanded service access, and stigma reduction. However, persistent technical challenges included potential algorithmic diagnostic errors, reliance on self-reported and screen-based data, and complex interface design. Ethical concerns included data security, privacy, limited transparency, attribution of responsibility, and deficiencies in informed consent processes.

CONCLUSIONS: AI can accurately identify and predict mental health risks, overcome geographical and temporal barriers in adolescent mental healthcare, enable efficient and personalized support, facilitate clinical management, and expand access for emergency crises, marginalized populations, and underserved areas. However, complex study protocols and severe mental health conditions may reduce adolescent engagement. The long-term sustainability of AI’s therapeutic effects requires further validation. Future efforts should focus on expanding the evidence base, validating interventions in real-world settings, strengthening regulatory frameworks, and enhancing the digital competencies of healthcare providers.

REVIEW REGISTRATION: Open Science Framework https://osf.io/79wya.

TRIAL REGISTRATION: Not applicable.

PMID:42177455 | DOI:10.1186/s12888-026-08190-y

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