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This research aims to establish a framework for managing chronic diseases using digital technologies and AI.

January 22, 2026Biomedicines0 citationsOpen Access

Digital-Intelligent Precision Health Management: An Integrative Framework for Chronic Disease Prevention and Control

YMYu MaCapital University DCDafang ChenPeking University JXJin XieCapital Medical University

Key Points

This research aims to establish a framework for managing chronic diseases using digital technologies and AI.
Synthesis of recent literature over the past three years.
Examination of existing digital technologies and their applications in health management.
Evaluation of translational and ethical challenges in implementing the framework.
Exploration of future directions for integrating this framework within healthcare systems.
Identified the need for multidimensional health-related phenotyping to capture individual health states.
Demonstrated the potential of machine learning in risk prediction and early diagnosis.
Outlined the integration of AI and digital twins for personalized intervention planning.

Abstract

Non-communicable diseases (NCDs) impose an overwhelming burden on global health systems. Prevailing healthcare for NCDs remains largely hospital-centered, episodic, and reactive, rendering them poorly suited to address the long-term, heterogeneous, and multifactorial nature of NCDs. Rapid advances in digital technologies, artificial intelligence (AI), and precision medicine have catalyzed the development of an integrative framework for digital-intelligent precision health management, characterized by the functional integration of data, models, and decision support. It is best understood as an integrated health management framework operating across three interdependent dimensions. First, it is grounded in multidimensional health-related phenotyping, enabled by continuous digital sensing, wearable and ambient devices, and multi-omics profiling, which together allow for comprehensive, longitudinal characterization of individual health states in real-world settings. Second, it leverages intelligent risk warning and early diagnosis, whereby multimodal data are fused using advanced machine learning algorithms to generate dynamic risk prediction, detect early pathological deviations, and refine disease stratification beyond conventional static models. Third, it culminates in health management under intelligent decision-making, integrating digital twins and AI health agents to support personalized intervention planning, virtual simulation, adaptive optimization, and closed-loop management across the disease continuum. Framed in this way, digital-intelligent precision health management enables a fundamental shift from passive care towards proactive, anticipatory, and individual-centered health management. This Perspectives article synthesizes recent literature from the past three years, critically examines translational and ethical challenges, and outlines future directions for embedding this framework within population health and healthcare systems.

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