Artificial Intelligence and Multi-Omics in Cardiovascular Diseases: A New Era of Precision Medicine

Cardiovascular diseases (CVDs) arise through interactions across genetic, molecular, cellular, anatomical and physiological scales, yet these dimensions are often studied in isolation. Integrating multi-omics with medical imaging, electrocardiography, electronic health records and wearable measurements can provide a more comprehensive view of cardiovascular disease than any single modality. Artificial intelligence (AI) provides a computational framework for analyzing these heterogeneous data and linking molecular alterations to disease phenotypes, clinical trajectories and therapeutic opportunities. However, progress is limited by sparse and incompletely matched datasets, cross-modal misalignment, limited interpretability, poor generalizability, and unresolved privacy and governance challenges. In this review, we examine the opportunities and limitations of AI-enabled multimodal integration in cardiovascular research and clinical translation. Overcoming these barriers is essential to establish AI-enabled multimodal integration as a robust foundation for more mechanistically informed and precise cardiovascular medicine.