Rethinking AI in youth mental health
Promise, perils, and ethical integration
Bibliographic Data
| ID | 22428469 |
|---|---|
| Authors | Andy Man Yeung Tai (0000-0001-5262-8615, University of British Columbia Hospital, corresponding author), Ian Hickie (The University of Sydney), Will Capon (The University of Sydney), Ante Prodan (0000-0001-9684-7967, The University of Sydney), Agam Sanghera (University of British Columbia), Michael Krausz (0000-0002-3509-7274, University of British Columbia), Frank Iorfino (0000-0003-1109-0972, The University of Sydney) |
| Year | 2026 |
| Volume | 20 |
| Issue | 1 |
| Publication date | 2026-06-29 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Mental Health Systems (JOURNAL) |
| Journal identifiers | ISSN: 1752-4458 • E-ISSN: 1752-4458 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1186/s13033-026-00714-z |
| PMID | 42374532 |
| OpenAlex | W7166442506 |
| Language | EN |
| References cited | 11 |
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.
Health care · Mental health · Mental healthcare · Mental illness · Scrutiny · Transformative learning · Artificial Intelligence in Healthcare and Education · Digital Mental Health Interventions · Mental Health via Writing
| Highly cited | No |
|---|