WEQA: Wearable hEalth Question Answering with Query-Adaptive Agentic Reasoning

2026-06-16 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Health & Medical Research · Depth: Expert, quick

Summary

The WEQA framework addresses the challenges of medical question answering using wearable health data, an area where traditional language models struggle due to the continuous, high-dimensional, and longitudinal nature of sensor data. WEQA is a query-adaptive agent framework that integrates LLM reasoning with specialized wearable analytical and modeling tools. An LLM controller dynamically synthesizes execution plans, routing each query to the appropriate combination of sensor analysis and pretrained models, and performs grounded response auditing using external knowledge. This approach overcomes the limitations of fixed reasoning workflows and single foundation models in handling diverse sensor modalities and user intents. Evaluated on a new benchmark comprising four open wearable datasets across three health domains, WEQA achieved 24% higher accuracy than existing LLM and agentic baselines. A blinded study involving 12 medical experts and 8 users further confirmed significant improvements in usefulness and clinical soundness.

Key takeaway

For AI Scientists and Machine Learning Engineers developing health AI, WEQA demonstrates that integrating LLMs with specialized analytical tools via a query-adaptive agent significantly improves performance on complex wearable data. You should consider adopting agentic frameworks that dynamically route queries to domain-specific models and external knowledge for robust medical question answering. This approach can yield substantial gains in accuracy, usefulness, and clinical soundness compared to standalone LLM solutions.

Key insights

WEQA is a query-adaptive agent framework that unifies LLM reasoning with specialized tools to accurately answer questions from complex wearable health data.

Principles

• LLMs need specialized tools for non-textual data.
• Dynamic routing improves complex data processing.
• Agentic frameworks enhance LLM medical QA.

Method

An LLM controller synthesizes execution plans, dynamically routing queries to specialized sensor analysis and pretrained models. It then performs grounded response auditing using external knowledge for accuracy.

In practice

• Design agentic systems for multimodal health data.
• Combine LLMs with specialized analytical tools.
• Benchmark agent performance on wearable datasets.

Topics

• Wearable Health Data
• Medical Question Answering
• Agentic AI
• Language Models
• Multimodal AI
• Health Data Analytics

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

Related on AIssential

• Towards a General Intelligence and Interface for Wearable Health Data — Large-scale pretraining on wearable data creates a foundation model for diverse health insights.
• Beyond Semantic Similarity: A Component-Wise Evaluation Framework for Medical Question Answering Systems with Health Equity Implications — Current LLM medical QA evaluations overlook critical accuracy and health equity risks.
• Exploring the feasibility of conversational diagnostic AI in a real-world clinical study — Supervised conversational AI for pre-visit history taking is feasible, safe, and well-received by patients and clinicians.
• An Agentic LLM-Based Framework for Population-Scale Mental Health Screening — An agentic LLM framework enhances mental health screening by ensuring robust, adaptable, and trustworthy AI pipelines.
• ArogyaSutra: A Multi-Agent Framework for Multimodal Medical Reasoning in Indic Languages — Multimodal medical reasoning in low-resource Indic languages benefits from specialized multi-agent frameworks and comprehensive multilingual datasets.
• Enhancing Clinical Trial Patient Matching through Knowledge Augmentation and Reasoning with Multi-Agent — Multi-agent systems can augment LLMs with domain knowledge to improve clinical trial patient matching accuracy.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.