VitalDiagnosis: AI-Driven Ecosystem for 24/7 Vital Monitoring and Chronic Disease Management

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.

Authors

• Zhikai Xue Department of Computer Science, Worcester Polytechnic Institute
• Tianqianjin Lin Department of Information Resources Management, Zhejiang University, Hangzhou, 310058, China Alibaba Group, Hangzhou, 311121, China
• Pengwei Yan Department of Information Resources Management, Zhejiang University, Hangzhou, 310058, China Alibaba Group, Hangzhou, 311121, China
• Ruichun Wang Worcester Polytechnic Institute
• Yuxin Liu
• Zhuoren Jiang Department of Information Resources Management, Zhejiang University, Hangzhou, 310058, China
• Xiaozhong Liu Computer Science Department, Worcester Polytechnic Institute, Worcester, 01609-2280, MA, USA

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