Rethinking AI in youth mental health: promise, perils, and ethical integration

The transformative role of Large Language Models (LLMs) in youth mental health care as intelligent agents that can support patient engagement and augment healthcare delivery has been explored in this commentary. By acting as personalized virtual assistants for both patients and providers, LLMs have the potential to offer real-time, context-aware support, improving administrative workflows within mental health services. However, there is increasing concern within the research community regarding overinflation of claims surrounding large language models in mental healthcare. Technology companies often overstate their capabilities, creating unrealistic expectations that may lead to detrimental outcomes for patients. This paper critically examines the opportunities and limitations, advocating for greater transparency and rigorous scrutiny in the application of AI within sensitive domains such as mental health. It underscores the need for developmentally appropriate engagement, cultural sensitivity, and the integration of human expertise alongside AI to ensure ethical and effective collaboration in mental healthcare interventions.

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Publication Details

Journal
International Journal of Mental Health Systems
Published
2026-06-29
DOI
https://doi.org/10.1186/s13033-026-00714-z
Primary Topic
Digital Mental Health Interventions
Type
article
Field-Weighted Citation Impact
0.00
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article

Rethinking AI in youth mental health: promise, perils, and ethical integration

Frank Iorfino, Andy Man Yeung Tai, Ante Prodan, Michael Krausz et al.
International Journal of Mental Health Systems
Digital Mental Health Interventions
article

Rethinking AI in youth mental health: promise, perils, and ethical integration

Frank Iorfino, Andy Man Yeung Tai, Ante Prodan, Michael Krausz, Agam Sanghera, Ian Hickie, Will Capon
article en

Abstract

The transformative role of Large Language Models (LLMs) in youth mental health care as intelligent agents that can support patient engagement and augment healthcare delivery has been explored in this commentary. By acting as personalized virtual assistants for both patients and providers, LLMs have the potential to offer real-time, context-aware support, improving administrative workflows within mental health services. However, there is increasing concern within the research community regarding overinflation of claims surrounding large language models in mental healthcare. Technology companies often overstate their capabilities, creating unrealistic expectations that may lead to detrimental outcomes for patients. This paper critically examines the opportunities and limitations, advocating for greater transparency and rigorous scrutiny in the application of AI within sensitive domains such as mental health. It underscores the need for developmentally appropriate engagement, cultural sensitivity, and the integration of human expertise alongside AI to ensure ethical and effective collaboration in mental healthcare interventions.

International Journal of Mental Health SystemsVol. 20(1)
University of British Columbia Hospital (CA), The University of Sydney (AU), University of British Columbia (CA)
Openalex Percentile: Top 8%
Digital Mental Health Interventions
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