Machine learning and multi-omics technologies for precision cardiovascular medicine: advancing diagnosis, risk prediction, and therapeutic guidance

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 spectrum of cardiovascular disease from molecular alterations to phenotypic manifestations, while machine learning is well-suited to modeling complex associations between high-dimensional, nonlinear omics data and clinical outcomes. Their integration has therefore opened new avenues for the precise management of cardiovascular disease. This review systematically summarizes recent advances in applying machine learning and multi-omics to precision cardiovascular medicine, with a focus on three core domains: diagnosis, risk prediction, and treatment response prediction. In diagnosis, these approaches can assist with definitive diagnosis, early detection, differential diagnosis, and severity assessment, thereby enabling more noninvasive, objective, rapid, and precise evaluation of cardiovascular disease. In risk prediction, they support a comprehensive framework spanning primary prevention, secondary prevention, short-term risk stratification, and screening of high-risk populations, allowing risk management across the full disease course and across diverse patient groups. In addition, they enable individualized prediction of the benefits and risks of pharmacological and surgical treatments, thereby informing therapeutic decision-making. To facilitate clinical translation, several challenges remain particularly important, including data quality control, continuous model validation, improved transparency, clarification of responsibility and accountability, and supportive policies regarding implementation and cost coverage.