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

2026-01-22

AI Summary

This work proposes a novel large language model (LLM)-driven paradigm for proactive chronic disease management that addresses key challenges including difficulty in early deterioration detection, low patient adherence, and strained healthcare resources. By uniquely integrating continuous physiological data from wearable devices with the contextual reasoning capabilities of LLMs, the system enables anomaly-triggered detection, generation of preliminary clinical insights, and delivery of personalized intervention recommendations. Embedded within a clinician–patient collaborative workflow, the framework supports real-time interaction and dynamic decision-making, significantly enhancing patients’ self-management capabilities and improving early identification of acute events, while simultaneously alleviating clinical workload.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Natural Language Processing: (Large) Language Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• chronic disease management
• vital monitoring
• patient adherence
• health deterioration
• aging population

Innovation

• LLM-driven ecosystem
• continuous vital monitoring
• context-aware inquiry
• proactive chronic disease management
• personalized health guidance