Large language models (LLMs) have attracted significant attention for potential applications in digital health, while their application in mental health is subject to ongoing debate. This systematic review aims to evaluate the usage of LLMs in mental health, focusing on their strengths and limitations in early screening, digital interventions, and clinical applications. Adhering to PRISMA guidelines, we searched PubMed, IEEE Xplore, Scopus, and the JMIR using keywords: 'mental health OR mental illness OR mental disorder OR psychiatry' AND 'large language models'. We included articles published between January 1, 2017, and December 31, 2023, excluding non-English articles. 30 articles were evaluated, which included research on mental illness and suicidal ideation detection through text (n=12), usage of LLMs for mental health conversational agents (CAs) (n=5), and other applications and evaluations of LLMs in mental health (n=13). LLMs exhibit substantial effectiveness in detecting mental health issues and providing accessible, de-stigmatized eHealth services. However, the current risks associated with the clinical use might surpass their benefits. The study identifies several significant issues: the lack of multilingual datasets annotated by experts, concerns about the accuracy and reliability of the content generated, challenges in interpretability due to the 'black box' nature of LLMs, and persistent ethical dilemmas. These include the lack of a clear ethical framework, concerns about data privacy, and the potential for over-reliance on LLMs by both therapists and patients, which could compromise traditional medical practice. Despite these issues, the rapid development of LLMs underscores their potential as new clinical aids, emphasizing the need for continued research and development in this area.

翻译：大型语言模型（LLMs）在数字健康领域的潜在应用已引起广泛关注，但其在心理健康领域的应用仍处于持续讨论之中。本系统性综述旨在评估LLMs在心理健康领域的使用情况，重点关注其在早期筛查、数字干预及临床应用中的优势与局限。遵循PRISMA指南，我们在PubMed、IEEE Xplore、Scopus及JMIR数据库中，以“心理健康或精神疾病或精神障碍或精神病学”与“大型语言模型”为关键词进行检索。纳入2017年1月1日至2023年12月31日期间发表的英文文献，排除非英文文章。最终对30篇文献进行评估，其中包括基于文本的精神疾病与自杀意念检测研究（12篇）、LLMs在心理健康对话代理（CAs）中的应用（5篇），以及LLMs在心理健康领域的其他应用与评估（13篇）。LLMs在检测心理健康问题及提供可访问、去污名化的电子健康服务方面表现出显著效能。然而，当前临床应用伴随的风险可能超过其收益。本研究识别出若干重要问题：缺乏专家标注的多语言数据集、对生成内容准确性与可靠性的担忧、因LLMs“黑箱”特性导致的可解释性挑战，以及持续存在的伦理困境。这些困境包括缺乏明确的伦理框架、数据隐私顾虑，以及治疗师与患者可能过度依赖LLMs而损害传统医疗实践的风险。尽管存在这些问题，LLMs的快速发展凸显了其作为新型临床辅助工具的潜力，强调了在该领域持续开展研发的必要性。