Yun Liu

Large language models are enabling a new generation of health agents, yet their ability to address the diverse needs of individuals in everyday health settings remains underexplored ^1,2 . Here we developed a comprehensive personal health agent that reasons over multimodal data from consumer wellness devices and medical records to provide personalized health support. Through analysis of web search and health forum queries, together with user-centred research involving end users and health experts, we identified four key user journeys: general health knowledge, personal data interpretation, wellness advice, and symptom assessment. These journeys informed three specialist sub-agents: (1) a data science agent that analyses longitudinal wearable and medical data, incorporating population-level statistics to contextualize numerical insights; (2) a health domain expert agent that integrates personal and contextual data with health knowledge to generate personalized insights; and (3) a health coach agent that synthesizes insights, supports multi-turn interaction and goal setting, applies specified psychological strategies and tracks progress. We integrated these capabilities in the personal health agent, an open-source multi-agent framework for dynamic, personalized health interactions. We evaluated the sub-agents and full system across 11 benchmark tasks using automated and human evaluation, with more than 7,000 annotations and 1,350 h of effort from health experts and end users. This represents the most comprehensive evaluation of a personal health agent thus far and provides a concrete foundation for improving access to high-quality personalized health support more broadly.

Artificial intelligence has high potential for impact in healthcare applications, but its training and deployment are challenging due to diverse data, a complex spectrum of possible tasks and important privacy needs. High-performing foundation models that enable data-efficient fine-tuning for diverse downstream tasks can meaningfully accelerate development in this domain. We introduce MedGemma, a collection of medical vision-language foundation models based on Gemma 3. MedGemma demonstrates advanced medical understanding and reasoning across images and text and multiple medical imaging domains, exceeding the performance of similarly sized generative models while maintaining the general capabilities of the Gemma base models. For out-of-distribution tasks, MedGemma achieves improvements of 2.6–10% in medical image question answering, 15.5–18.1% in chest X-ray finding classification and 10.8% in agentic evaluations compared with the base models. Our results show that fine-tuning MedGemma can be more effective than fine-tuning the base Gemma 3 model for medical tasks, particularly in the setting of limited training data. We additionally introduce MedSigLIP, a medically tuned vision encoder derived from SigLIP. MedSigLIP powers the visual understanding capabilities of MedGemma and, as an encoder, achieves performance comparable to or better than that of many specialized medical image encoders. Taken together, the MedGemma collection provides a strong foundation of medical image and text capabilities, with the potential to accelerate medical research and the development of downstream applications.

Language models excel at diagnostic assessments on curated medical case-studies and vignettes, performing on par with, or better than, clinical professionals. However, existing studies focus on complex scenarios with rich context making it difficult to draw conclusions about how these systems perform for patients reporting symptoms in everyday life. We deployed SymptomAI, a set of conversational AI agents for end-to-end patient interviewing and differential diagnosis (DDx), via the Fitbit app in a study that randomized participants (N=13,917) to interact with five AI agents. This corpus captures diverse communication and a realistic distribution of illnesses from a real world population. A subset of 1,228 participants reported a clinician-provided diagnosis, and 517 of these were further evaluated by a panel of clinicians during over 250 hours of annotation. SymptomAI DDx were significantly more accurate (OR = 2.56, p < 0.001) than those from independent clinicians given the same dialogue in a blinded randomized comparison. Moreover, agentic strategies which conduct a dedicated symptom interview that elicit additional symptom information before providing a diagnosis, perform substantially better than baseline, user-guided conversations (p < 0.001). An auxiliary analysis on 1,509 conversations from a general US population panel validated that these results generalize beyond wearable device users. We used SymptomAI diagnoses as labels for all 13,917 participants to analyze over 500,000 days of wearable metrics across nearly 400 unique conditions. We identified strong associations between acute infections and physiological shifts (e.g., OR > 7 for influenza). While limited by self-reported ground truth, these results demonstrate the benefits of a dedicated and complete symptom interview compared to a user-guided symptom discussion, which is the default of most consumer LLMs.

Resting heart rate (RHR) is a key biomarker of cardiovascular health and mortality^1,2,3, but passively tracking it longitudinally generally requires a wearable device, limiting its availability. Here we present passive heart-rate monitoring (PHRM), a deep-learning system that uses facial video-based photoplethysmography for passive measurements of heart rate (HR) and RHR during everyday smartphone interactions. Our system was developed using 192,353 videos from 485 participants and validated on 162,546 videos from 211 participants in laboratory and free-living conditions, representing, to our knowledge, the largest validation study of its kind. PHRM outperformed state-of-the-art methods on our benchmarks. Compared with reference electrocardiograms, PHRM achieved a mean absolute percentage error (MAPE) lower than 10% for HR measurements across three skin-tone groups of light, medium and dark pigmentation, meeting industry accuracy standards; MAPE for each skin-tone group was non-inferior versus the others. Daily RHR measured by PHRM had a mean absolute error of less than five beats per minute, compared with a wearable HR tracker, and was associated with known risk factors for cardiovascular disease. These results highlight the potential of smartphones for enabling passive and equitable monitoring of heart health. To facilitate further research, we publicly release a large, annotated smartphone video dataset along with a pre-trained HR model.

Real-world clinical practice is inherently multimodal, relying on the synthesis of patient history with visual information such as medical imagery and clinical documents. Although large language models (LLMs) have shown promise in diagnostic dialogue, their evaluation has been largely restricted to text-only interactions, failing to capture the complexity of modern remote care delivery. Here we introduce a multimodal extension of the Articulate Medical Intelligence Explorer (multimodal AMIE), capable of gathering, interpreting and reasoning about multimodal data within a diagnostic conversation. To achieve this, we developed a state-aware dialogue framework that dynamically guides history-taking based on diagnostic uncertainty and evolving patient states, emulating the structured reasoning of experienced clinicians. We evaluated this updated, state-aware version of multimodal AMIE against primary care physicians (PCPs) in a randomized, blinded exploratory study comprising 105 simulated telehealth consultations, which included dermatology photographs, electrocardiograms and clinical documents. As assessed by 18 specialist physicians, multimodal AMIE outperformed PCPs not only in diagnostic accuracy but also in conversation quality, including history-taking and empathy. Specifically, multimodal AMIE demonstrated superior performance on 29 of 32 evaluation axes, including seven of nine metrics that assess multimodal reasoning. These results validate the efficacy of state-aware reasoning in bridging the gap between text and visual information and demonstrate the potential for artificial intelligence (AI) systems to augment clinicians in complex, multimodal diagnostic settings.