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<p>Large language models (LLMs) show promise in mental health care for handling human-like conversations, but their effectiveness remains uncertain. This scoping review synthesizes existing research on LLM applications in mental health care, reviews model performance and clinical effectiveness, identifies gaps in current evaluation methods following a structured evaluation framework, and provides recommendations for future development. A systematic search identified 726 unique articles, of which 16 met the inclusion criteria. These studies, encompassing applications such as clinical assistance, counseling, therapy, and emotional support, show initial promises. However, the evaluation methods were often non-standardized, with most studies relying on ad-hoc scales that limit comparability and robustness. A reliance on prompt-tuning proprietary models, such as OpenAI’s GPT series, also raises concerns about transparency and reproducibility. As current evidence does not fully support their use as standalone interventions, more rigorous development and evaluation guidelines are needed for safe, effective clinical integration.</p>

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A scoping review of large language models for generative tasks in mental health care

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年4月30日

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  • Yining Hua,
  • Hongbin Na,
  • Zehan Li,
  • Fenglin Liu,
  • Xiao Fang,
  • David Clifton,
  • John Torous

摘要

Large language models (LLMs) show promise in mental health care for handling human-like conversations, but their effectiveness remains uncertain. This scoping review synthesizes existing research on LLM applications in mental health care, reviews model performance and clinical effectiveness, identifies gaps in current evaluation methods following a structured evaluation framework, and provides recommendations for future development. A systematic search identified 726 unique articles, of which 16 met the inclusion criteria. These studies, encompassing applications such as clinical assistance, counseling, therapy, and emotional support, show initial promises. However, the evaluation methods were often non-standardized, with most studies relying on ad-hoc scales that limit comparability and robustness. A reliance on prompt-tuning proprietary models, such as OpenAI’s GPT series, also raises concerns about transparency and reproducibility. As current evidence does not fully support their use as standalone interventions, more rigorous development and evaluation guidelines are needed for safe, effective clinical integration.