The past decade has been transformative for mental health research and
practice. The ability to harness large repositories of data, whether from
electronic health records (EHR), mobile devices, or social media, has revealed
a potential for valuable insights into patient experiences, promising early,
proactive interventions, as well as personalized treatment plans. Recent
developments in generative artificial intelligence, particularly large language
models (LLMs), show promise in leading digital mental health to uncharted
territory. Patients are arriving at doctors' appointments with information
sourced from chatbots, state-of-the-art LLMs are being incorporated in medical
software and EHR systems, and chatbots from an ever-increasing number of
startups promise to serve as AI companions, friends, and partners. This article
presents contemporary perspectives on the opportunities and risks posed by LLMs
in the design, development, and implementation of digital mental health tools.
We adopt an ecological framework and draw on the affordances offered by LLMs to
discuss four application areas -- care-seeking behaviors from individuals in
need of care, community care provision, institutional and medical care
provision, and larger care ecologies at the societal level. We engage in a
thoughtful consideration of whether and how LLM-based technologies could or
should be employed for enhancing mental health. The benefits and harms our
article surfaces could serve to help shape future research, advocacy, and
regulatory efforts focused on creating more responsible, user-friendly,
equitable, and secure LLM-based tools for mental health treatment and
intervention.

近年来，大型语言模型（LLM）在数字精神健康工具设计、开发和实施领域展示了潜在的机遇和风险，我们提出了四个应用领域，包括对需要关怀的个体的求助行为、社区关怀提供、机构和医疗关怀提供，以及更大规模的社会关怀生态系统，并思考了 LLM 技术如何提升心理健康。本文的发现有助于未来研究、倡导和监管工作，以创建更负责任、用户友好、公平和安全的基于 LLM 的精神健康治疗和干预工具。