AI 中文总结
本综述提出LLMs在心理健康中经历从被动模式识别器到共情对话者再到个性化伴侣的三阶段演进框架，系统梳理核心技术、代理架构与基准，为未来人本AI心理健康研究提供路线图。
AI 中文摘要
全球心理健康问题的日益普遍，加上传统医疗中长期存在的障碍，如资源有限、成本高昂、污名化和隐私问题，迫切需要对可获取且可扩展的支持。大语言模型（LLMs）已成为一种变革性技术，通过先进的自然语言理解和生成能力，在普及心理健康支持方面具有巨大潜力。然而，该领域快速扩展但碎片化的研究缺乏连贯的演进叙事，使得难以将当前进展置于背景中并识别未来方向。本综述通过围绕一个核心论点组织和分析文献来解决这一空白：LLMs在心理健康中的角色正经历三个不同且日益复杂的阶段。我们追溯这一轨迹：从第一阶段（LLMs主要作为被动信息工具和模式识别器用于评估），到第二阶段（它们作为共情对话者进行即时、无状态交互），再到当前前沿的第三阶段（寻求作为有状态认知代理实现的纵向、个性化伴侣）。为支持这一框架，我们系统回顾了核心技术、代理架构（配置文件、记忆、推理和规划）以及数据集和基准的关键基础设施，强调其演进如何支撑这一发展路径。通过这一发展视角审视该领域，我们提供了对现有工作的全面综合、对其轨迹的深刻叙述，以及未来在负责任、有效和以人为中心的心理健康医疗AI创新中的清晰路线图。本综述中回顾的精选资源集合可在我们的项目仓库中获取：此HTTPS URL。
英文摘要
The rising global prevalence of mental health conditions, together with longstanding barriers in traditional healthcare, such as limited resources, high cost, stigma, and privacy concerns, has created an urgent need for accessible and scalable support. Large Language Models (LLMs) have emerged as a transformative technology with strong potential to democratize mental health support through advanced natural language understanding and generation. However, the rapidly expanding, fragmented body of work in this area lacks a coherent evolutionary narrative, making it difficult to contextualize current progress and identify future directions. This survey addresses this gap by organizing and analyzing the literature around a central thesis: the role of LLMs in mental health is evolving through three distinct, increasingly sophisticated phases. We trace this trajectory from Phase I, in which LLMs act primarily as passive Information Tools and Pattern Recognizers for assessment; through Phase II, where they function as Empathetic Conversationalists for in-the-moment, stateless interactions; to the current frontier, Phase III, which seeks Longitudinal, Personalized Companions implemented as stateful cognitive agents. To support this framework, we systematically review core technologies, agent architectures (Profile, Memory, Reasoning, and Planning), and the critical infrastructure of datasets and benchmarks, highlighting how their evolution underpins this developmental path. Viewing the field through this developmental lens, we provide a comprehensive synthesis of existing work, an insightful narrative of its trajectory, and a clear roadmap for future innovation in responsible, effective, and human-centered AI for mental healthcare. A curated collection of the resources reviewed in this survey is available at our project repository: https://github.com/Emo-gml/Awesome-Mental-Health-LLMs.
