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.

翻译：慢性疾病已成为全球主要死因，而紧张的医疗资源与人口老龄化加剧了这一挑战。患者个体往往难以解读病情恶化的早期征兆或持续遵循护理计划。本文提出VitalDiagnosis——一个基于大语言模型（LLM）的生态系统，旨在将慢性病管理从被动监测转变为主动交互式参与。该系统通过整合可穿戴设备的连续数据与LLM的推理能力，同时应对急性健康异常与日常依从性问题。它借助情境感知查询分析触发因素，在医患协同工作流中生成阶段性见解，并提供个性化指导。该方法致力于推动更具前瞻性与协作性的医疗范式，有望提升患者自我管理能力并减少可避免的临床工作负担。