Large language models (LLMs) have received much attention and shown their
potential in digital health, while their application in mental health is
subject to ongoing debate. This systematic review aims to summarize and
characterize the use of LLMs in mental health by investigating the strengths
and limitations of the latest work in LLMs and discusses the challenges and
opportunities for early screening, digital interventions, and other clinical
applications in mental health. Following PRISMA guidelines, we examined English
articles from PubMed, DBLP Computer Science Bibliography, and IEEE Xplore,
published between 1 January 2017, and 1 September 2023, focusing on mental
health and LLMs. The review analyzed 32 articles, including mental health
analysis using social media datasets (n=13), mental health chatbots (n=10), and
other mental health applications (n=9). Findings reveal LLMs' effectiveness in
mental health issue detection and the enhancement of telepsychological services
through personalised healthcare. Nonetheless, risks like text inconsistencies,
hallucinatory content, and the lack of an ethical framework raise concerns
about their clinical use. Despite these challenges, the advancement of LLMs
underscores their potential as innovative clinical tools, necessitating further
research and development. The review emphasizes that LLMs should complement,
not replace, professional mental health services.

总结和概括了大型语言模型（LLMs）在心理健康领域的应用，包括早期筛查、数字干预和其他临床应用领域的强项、限制、挑战和机遇，并指出了 LLMs 在心理健康问题检测和个性化医疗方面的有效性，同时也提出了关于文本一致性、幻觉内容和缺乏伦理框架的风险以及 LLMs 作为创新临床工具的进一步研究和发展的必要性，强调 LLMs 应该是专业心理健康服务的补充而非替代。

大型语言模型用于心理健康研究的系统综述

Large Language Model for Mental Health: A Systematic Review

Online reinforcement learning (RL) algorithms are increasingly used to
personalize digital interventions in the fields of mobile health and online
education. Common challenges in designing and testing an RL algorithm in these
settings include ensuring the RL algorithm can learn and run stably under
real-time constraints, and accounting for the complexity of the environment,
e.g., a lack of accurate mechanistic models for the user dynamics. To guide how
one can tackle these challenges, we extend the PCS (Predictability,
Computability, Stability) framework, a data science framework that incorporates
best practices from machine learning and statistics in supervised learning (Yu
and Kumbier, 2020), to the design of RL algorithms for the digital
interventions setting. Further, we provide guidelines on how to design
simulation environments, a crucial tool for evaluating RL candidate algorithms
using the PCS framework. We illustrate the use of the PCS framework for
designing an RL algorithm for Oralytics, a mobile health study aiming to
improve users' tooth-brushing behaviors through the personalized delivery of
intervention messages. Oralytics will go into the field in late 2022.

该研究介绍了如何使用预测性、可计算性和稳定性（PCS）框架设计在线强化学习算法来个性化数字干预，提供了指南并展示了如何在模拟环境中评估候选算法，以加强现实时间限制和用户动态环境的稳定性，从而在 Oralytics 项目中改善用户的口腔健康，预计 2022 年底进行实地测试。