Artificial Intelligence and Multi-Omics Integration for Precision Cardiovascular Medicine: A Narrative Review

Atherosclerotic cardiovascular disease is among the leading causes of death worldwide, and technological advances have made it possible to expand the repertoire of biomarkers used in diagnostics and treatment. These include different omics such as genomics, epigenomics, transcriptomics, proteomics, and metabolomics. The combination of multi-omic data with machine learning and artificial intelligence has emerged as a useful tool for the molecular phenotyping of atherosclerotic cardiovascular disease. Artificial intelligence has the potential to transform every facet of cardiovascular practice and research, with innovations that span novel diagnostic modalities, new digital native biomarkers of disease, and high-performing tools evaluating care quality and prognosticating clinical outcomes. The integration of multi-omics technologies with artificial intelligence has become a transformative force in modern precision medicine, particularly within drug discovery. AI models integrating multi-omics data have improved cardiovascular risk prediction by up to ۲۵% compared to traditional risk scores. This review discusses the usefulness of different omic data and their integration by artificial intelligence and machine learning strategies for accurate identification, diagnosis, and treatment of cardiovascular disease.

کلیدواژه ها:

Artificial intelligence ، Multi-omics ، Cardiovascular disease ، Precision medicine ، Machine learning ، Biomarkers