Title: DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home
Primary area: foundation or frontier models, including LLMs
Keywords: Large Language Model, Large Vision Language Model, Healthcare, Diagnosis-It-Yourself, Home Care

Abstract: Generative AI is reshaping healthcare by enhancing multimodal data interpretation, clinical insight generation, and personalized decision support. However, existing advances remain tightly coupled with hospital-grade devices, restricting accessibility and use for anytime, anywhere health management in non-clinical settings. With the proliferation of wearables, mobile sensors, and telemedicine, healthcare is
shifting toward the home, giving rise to the emerging field of Diagnosis-It-Yourself (DIY) at home, i.e., home care. Despite this promise, several distinctive challenges remain: (i) home-collected data are heterogeneous, exacerbated by the absence of standardized large-scale datasets; (ii) models require adaptation to highly variable task demands and dynamically evolving individual conditions; (iii) the broad spectrum of home care tasks lacks a unified benchmark for systematic evaluation. In this paper, we present DIYHealth Suite, a comprehensive framework designed to address these challenges through a tailored dataset, model, and benchmark. We first curate DIYHealth-900K, a large-scale multimodal dataset capturing diverse real-world home care scenarios. Building on this, we propose DIYHealthGPT, an adaptive foundation model for home-based health management, powered by the novel Hybrid Hyper Low-Rank Adaptation technique, which integrates expert mixtures with hypernetwork-driven modulation to balance cross-task generalization and instance-level personalization. Finally, we establish DIYHealthBench, the first benchmark to evaluate foundation models on home care tasks. Extensive experiments demonstrate that DIYHealthGPT delivers state-of-the-art performance over both general-purpose and medical-specific baselines on 11 home care tasks in both open-QA and closed-QA settings, laying the groundwork for the next generation of AI-driven, personalized, and scalable health management at home.