构建可个性化健康建议的多智能体系统，支持日常健康管理。
The Anatomy of a Personal Health Agent
- 设计三类专用智能体：数据分析师、健康专家与心理教练。
- 在10个任务中完成超7000次标注与1100小时专家评估。
- 面向普通用户与健康从业者，适合长期健康管理场景。
健康是人类福祉的核心，大语言模型（LLMs）的快速发展推动了新一代健康助手的诞生。然而，这类助手在非临床日常场景中满足个体多样化需求的研究仍较薄弱。本文旨在构建一个全面的个人健康代理（PHA），能够分析来自可穿戴设备和健康记录的多模态数据，并提供个性化建议。通过深入分析网络搜索与健康论坛查询，并结合用户与专家的定性反馈，我们识别出三大类消费者健康需求，分别由三类专业子代理支持：（1）数据分析代理，处理时间序列健康数据；（2）健康领域专家代理，整合健康与上下文信息生成精准洞察；（3）健康教练代理，基于心理策略引导用户并追踪进展。我们提出了个人健康代理（PHA）多智能体框架，实现动态个性化交互。为评估各子代理及整体系统，我们在10项基准任务中进行了自动化与人工评估，累计超过7000次标注与1100小时专家与用户投入。本研究是迄今最全面的健康代理评估，为人人可及的未来健康助手奠定坚实基础。
原文摘要 · Abstract (English)
Health is a fundamental pillar of human wellness, and the rapid advancements in large language models (LLMs) have driven the development of a new generation of health agents. However, the application of health agents to fulfill the diverse needs of individuals in daily non-clinical settings is underexplored. In this work, we aim to build a comprehensive personal health agent that is able to reason about multimodal data from everyday consumer wellness devices and common personal health records, and provide personalized health recommendations. To understand end-users' needs when interacting with such an assistant, we conducted an in-depth analysis of web search and health forum queries, alongside qualitative insights from users and health experts gathered through a user-centered design process. Based on these findings, we identified three major categories of consumer health needs, each of which is supported by a specialist sub-agent: (1) a data science agent that analyzes personal time-series wearable and health record data, (2) a health domain expert agent that integrates users' health and contextual data to generate accurate, personalized insights, and (3) a health coach agent that synthesizes data insights, guiding users using a specified psychological strategy and tracking users' progress. Furthermore, we propose and develop the Personal Health Agent (PHA), a multi-agent framework that enables dynamic, personalized interactions to address individual health needs. To evaluate each sub-agent and the multi-agent system, we conducted automated and human evaluations across 10 benchmark tasks, involving more than 7,000 annotations and 1,100 hours of effort from health experts and end-users. Our work represents the most comprehensive evaluation of a health agent to date and establishes a strong foundation towards the futuristic vision of a personal health agent accessible to everyone.
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