用大模型连接可穿戴设备，实现慢性病全天候主动管理
VitalDiagnosis: AI-Driven Ecosystem for 24/7 Vital Monitoring and Chronic Disease Management
- 用大模型分析可穿戴数据，主动识别健康异常
- 通过追问与推理，生成患者-医生协作的初步判断
- 适合需要长期健康管理的慢病患者和医疗资源紧张地区
慢性病已成为全球首要死因，加剧于医疗资源紧张与人口老龄化。患者常难以察觉早期恶化迹象或坚持治疗方案。本文提出VitalDiagnosis，一个由大语言模型驱动的生态系统，将慢性病管理从被动监测转向主动互动。系统整合可穿戴设备的连续数据与大模型的推理能力，既能应对急性健康异常，也能监督日常依从性。通过情境感知的提问，生成初步洞察，并在患者-医生协作流程中提供个性化建议。该模式旨在推动更主动、协同的照护方式，提升患者自我管理能力，减轻可避免的临床负担。
原文摘要 · Abstract (English)
Chronic diseases have become the leading cause of death worldwide, a challenge intensified by strained medical resources and an aging population. Individually, patients often struggle to interpret early signs of deterioration or maintain adherence to care plans. In this paper, we introduce VitalDiagnosis, an LLM-driven ecosystem designed to shift chronic disease management from passive monitoring to proactive, interactive engagement. By integrating continuous data from wearable devices with the reasoning capabilities of LLMs, the system addresses both acute health anomalies and routine adherence. It analyzes triggers through context-aware inquiries, produces provisional insights within a collaborative patient-clinician workflow, and offers personalized guidance. This approach aims to promote a more proactive and cooperative care paradigm, with the potential to enhance patient self-management and reduce avoidable clinical workload.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。