aceMachine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance
Abstract: Cardiovascular disease remains a major global health burden. Owing to its complex pathogenesis and marked clinical heterogeneity, conventional one-size-fits-all strategies often yield limited benefit for a substantial proportion of patients. Precision medicine advocates individualized management based on patients' clinical and molecular characteristics to improve outcomes. In this context, multi-omics and machine learning provide critical technical support for precision medicine: multi-omics can capture the full…
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Published August 13, 2026 as a peer-reviewed review in Frontiers in Cardiovascular Medicine, the article synthesizes recent advances using machine learning together with multi-omics to address cardiovascular disease heterogeneity where conventional one-size-fits-all strategies often yield limited benefit.
The synthesis matters because it links technical capabilities to clinical tasks like noninvasive diagnosis and full-course risk management and individualized treatment prediction, while uncertainty remains about how to ensure data quality, ongoing validation, transparency, accountability, and policy support for cost coverage and implementation.
- Review focuses on three core domains: diagnosis, risk prediction, and treatment response prediction for cardiovascular disease.
- Multi-omics captures spectrum from molecular alterations to phenotypic manifestations while machine learning models high-dimensional nonlinear associations with clinical outcomes.
- In risk prediction, approaches support primary prevention, secondary prevention, short-term risk stratification, and screening of high-risk populations.
- Authors note precision medicine aims to replace one-size-fits-all strategies that often yield limited benefit due to clinical heterogeneity.
Integration of multi-omics and machine learning can improve cardiovascular disease management by supporting definitive and early diagnosis, severity assessment, full-course risk stratification, and individualized prediction of drug and surgical benefit-risk to inform decisions.
The rundown
The review describes multi-omics as capturing the full spectrum from molecular alterations to phenotypic manifestations, paired with machine learning suited to high-dimensional nonlinear data, applied to diagnosis for definitive, early, differential and severity assessment.
For risk prediction it outlines a framework spanning primary and secondary prevention, short-term stratification and high-risk screening, and for therapy it discusses individualized prediction of pharmacological and surgical benefits and risks to inform decision-making.
Sources
- Peer-reviewedFrontiers in Cardiovascular Medicine2026-08-13
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