DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home

• URL: http://arxiv.org/abs/2606.07542v1
• Date: Fri, 01 May 2026 17:28:59 GMT
• Title: DIYHealth Suite: Dataset, Model, and Benchmark for Health Management at Home
• Abstract summary: Generative AI is reshaping healthcare, yet most existing advances rely on hospital-grade devices.<n>With the proliferation of portable devices and telemedicine, healthcare is shifting toward home-based Diagnosis-It-Yourself care.<n>We present DIYHealth Suite, a comprehensive framework designed to address these challenges through a tailored dataset, model, and benchmark.
• Score: 39.50862243127376
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative AI is reshaping healthcare, yet most existing advances rely on hospital-grade devices, which limits their accessibility and potential for health management outside clinical settings. With the proliferation of portable devices and telemedicine, healthcare is shifting toward home-based Diagnosis-It-Yourself (DIY) 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 variable task demands and 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. 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 personalized health management at home.

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