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

Zhikai Xue, Tianqianjin Lin, Pengwei Yan, Ruichun Wang, Yuxin Liu +2 more
1/22/2026
cs.AI

No official code found. Best implementation: TruthSeeker99/VitalDiagnosis (1 star, Apache-2.0, last commit 2026-01-23).

Abstract

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.

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Code Implementations(1)

Apache-2.0

The official repository of VitalDiagnosis: AI-Driven Ecosystem for 24/7 Vital Monitoring and Chronic Disease Management. We provide supplementary materials here.

10Nov 13, 20259 months agoApache-2.0

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Cite this paper

@article{xue2026vitaldiagnosis,
  title  = {VitalDiagnosis: AI-Driven Ecosystem for 24/7 Vital Monitoring and Chronic Disease Management},
  author = {Zhikai Xue and Tianqianjin Lin and Pengwei Yan and Ruichun Wang and Yuxin Liu and Zhuoren Jiang and Xiaozhong Liu},
  year   = {2026},
  eprint = {2601.15798},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2601.15798v1}
}

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