Abstract
Language models are remarkably capable at medical question answering, sometimes surpassing the accuracy of general physicians. However, answering questions about wearable health data remains challenging and understudied, as these ubiquitous sensors produce continuous, high-dimensional, and longitudinal data, which is non-trivial to align with text-centric distributions in LLM pretraining. The diversity of sensor modalities and user intents cannot be effectively handled by a fixed reasoning workflow or a single pretrained foundation model.
To address these challenges, we propose WEQA, a query-adaptive agent framework that unifies LLM reasoning with specialized wearable analytical and modeling tools. An LLM controller is employed to synthesize execution plans and dynamically route each query to the appropriate combination of sensor analysis and pretrained models, while performing grounded response auditing with external knowledge.
We also curate a benchmark spanning four open wearable datasets comprising analytic and predictive tasks in three different health domains. Experiments show that our framework is 24% more accurate than LLM and agentic baselines, and a blinded study with 12 medical experts and 8 users shows substantial gains in usefulness and clinical soundness.
Blogger's Review: WEQA showcases a novel direction in personalized medical question answering by deeply integrating LLM with wearable technology. Its query-adaptive design not only enhances accuracy but also improves user experience, indicating the potential for future intelligent health management. The effectiveness and scalability in real-world applications warrant close attention.