The rapid advancement of large language models (LLMs), combined with data from wearable devices, presents a transformative opportunity to empower people on their personal health journeys. However, health needs vary from individual to individual. Answering a specific query, such as, “On average, how many hours have I been sleeping this last month?” requires different skills than an open-ended question like, “What can I do to improve my sleep quality?” A single system can struggle to address this complexity.
To meet this challenge, we adopt a human-centered process and propose the Personal Health Agent (PHA). This agent is a comprehensive research framework that can reason about multimodal data to provide personalized, evidence-based guidance. Using a multi-agent architecture, PHA deconstructs personal health and wellness support into three core roles (data science, domain expert, and health coach), each handled by a specialist sub-agent. To evaluate each sub-agent and the multi-agent system, we leveraged a real-world dataset from an IRB-reviewed study where ~1200 users provided informed consent to share their wearables data from Fitbit, a health questionnaire, and blood test results. 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.
This work outlines a conceptual framework for research purposes, and should not be considered a description of any specific product, service, or feature currently in development or available to the public. Any real-world application would be subject to a separate design, validation, and review process.
