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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月29日

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

摘要

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.