{"ID":2877680,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.20148","arxiv_id":"2508.20148","title":"The Anatomy of a Personal Health Agent","abstract":"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.","short_abstract":"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 wo...","url_abs":"https://arxiv.org/abs/2508.20148","url_pdf":"https://arxiv.org/pdf/2508.20148v2","authors":"[\"A. Ali Heydari\",\"Ken Gu\",\"Vidya Srinivas\",\"Hong Yu\",\"Zhihan Zhang\",\"Yuwei Zhang\",\"Akshay Paruchuri\",\"Qian He\",\"Hamid Palangi\",\"Nova Hammerquist\",\"Ahmed A. Metwally\",\"Brent Winslow\",\"Yubin Kim\",\"Kumar Ayush\",\"Yuzhe Yang\",\"Girish Narayanswamy\",\"Maxwell A. Xu\",\"Jake Garrison\",\"Amy Armento Lee\",\"Jenny Vafeiadou\",\"Ben Graef\",\"Isaac R. Galatzer-Levy\",\"Erik Schenck\",\"Andrew Barakat\",\"Javier Perez\",\"Jacqueline Shreibati\",\"John Hernandez\",\"Anthony Z. Faranesh\",\"Javier L. Prieto\",\"Connor Heneghan\",\"Yun Liu\",\"Jiening Zhan\",\"Mark Malhotra\",\"Shwetak Patel\",\"Tim Althoff\",\"Xin Liu\",\"Daniel McDuff\",\"Xuhai \\\"Orson\\\" Xu\"]","published":"2025-08-27T14:38:46Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.HC\",\"cs.MA\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}