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Health·G Space·Evidence-backed gain·Published 2026-07-22

An Integrative Review of the Cardiovascular Disease Spectrum: Integrating Multi-Omics and Artificial Intelligence for Precision Cardiology

BACKGROUND/OBJECTIVES: Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide and increasingly are recognized as a continuum of interconnected conditions rather than isolated entities. METHODS: A structured narrative literature search was performed in PubMed, Scopus, and Google Scholar for publications from 2015 to 2025 using combinations of different keywords: "cardiovascular disease spectrum", "multi-omics", "precision cardiology", "machine learning", and "artificial intel…

TRV-2026-0472Peer-reviewedPermanent record — cite & verify
An Integrative Review of the Cardiovascular Disease Spectrum: Integrating Multi-Omics and Artificial Intelligence for Precision Cardiology

Shaher2 by Giardello. CC BY-SA 3.0 · https://creativecommons.org/licenses/by-sa/3.0

The quick read

On 2026-01-13, a peer-reviewed integrative review in Diseases synthesized 2015-2025 literature on cardiovascular diseases as an interconnected continuum, examining how multi-omics data combined with AI-enabled imaging and digital tools are applied across seven major condition clusters.

The synthesis matters because it links mechanism-level biology to clinical actions like risk prediction and individualized prevention, but the source itself flags that benefits depend on unresolved issues of data quality, equity, interpretability, and routine-care implementation.

Main points
  • Structured narrative search covered PubMed, Scopus, and Google Scholar for 2015 to 2025 using keywords including cardiovascular disease spectrum and artificial intelligence in cardiology.
  • Evidence was synthesized across seven major clusters of cardiovascular conditions with common biological pathways mapped onto heterogeneous clinical phenotypes.
  • Authors frame cardiovascular diseases as a continuum rather than isolated entities to support mechanism- and data-driven precision cardiology.
Gain

Integrating multi-omics with AI-enabled imaging and digital tools improves risk prediction and informs clinical decision-making across interconnected cardiovascular conditions.

The rundown

The review searched PubMed, Scopus, and Google Scholar for 2015-2025 literature on cardiovascular disease spectrum, multi-omics, precision cardiology, machine learning and AI in cardiology, then synthesized findings across seven major condition clusters.

Across clusters, the authors mapped shared biological pathways to diverse phenotypes and described how multi-omics integration combined with AI-enabled imaging and digital tools is being used to refine risk stratification and guide decisions, while noting implementation challenges.

Sources

Reader signal

How should this claim be treated?

The debate