Google via Magnit, Folsom, CA, United States.
Google LLC, Mountain View, CA, 90291, United States, 13103106000.
JMIR Ment Health. 2024 Jul 29;11:e59479. doi: 10.2196/59479.
Global rates of mental health concerns are rising, and there is increasing realization that existing models of mental health care will not adequately expand to meet the demand. With the emergence of large language models (LLMs) has come great optimism regarding their promise to create novel, large-scale solutions to support mental health. Despite their nascence, LLMs have already been applied to mental health-related tasks. In this paper, we summarize the extant literature on efforts to use LLMs to provide mental health education, assessment, and intervention and highlight key opportunities for positive impact in each area. We then highlight risks associated with LLMs' application to mental health and encourage the adoption of strategies to mitigate these risks. The urgent need for mental health support must be balanced with responsible development, testing, and deployment of mental health LLMs. It is especially critical to ensure that mental health LLMs are fine-tuned for mental health, enhance mental health equity, and adhere to ethical standards and that people, including those with lived experience with mental health concerns, are involved in all stages from development through deployment. Prioritizing these efforts will minimize potential harms to mental health and maximize the likelihood that LLMs will positively impact mental health globally.
全球范围内的心理健康问题日益严重，人们越来越意识到现有的心理健康护理模式无法充分扩大规模以满足需求。随着大型语言模型（LLM）的出现，人们对其创造新颖的大规模解决方案以支持心理健康的潜力充满了乐观。尽管它们还处于起步阶段，但 LLM 已经被应用于与心理健康相关的任务。在本文中，我们总结了关于使用 LLM 提供心理健康教育、评估和干预的现有文献，并强调了每个领域产生积极影响的关键机会。然后，我们突出了与 LLM 在心理健康中的应用相关的风险，并鼓励采用减轻这些风险的策略。对心理健康支持的迫切需求必须与对心理健康 LLM 的负责任的开发、测试和部署相平衡。特别重要的是要确保针对心理健康对 LLM 进行微调，增强心理健康公平性，并遵守道德标准，让包括有心理健康问题经历的人在内的所有人都参与从开发到部署的所有阶段。优先考虑这些努力将最大限度地减少潜在的心理健康危害，并最大限度地提高 LLM 对全球心理健康产生积极影响的可能性。
