Precision Cardiovascular Medicine: Integrating Pharmacogenomics, Biomarkers, Artificial Intelligence, and Real-World Evidence

Cardiovascular disease (CVD) remains a major global cause of morbidity and mortality, with substantial interindividual variation in disease risk, treatment response, and susceptibility to adverse effects. Precision cardiovascular medicine incorporates patient-level information to refine cardiovascular prevention, diagnosis, risk stratification, and treatment. This structured narrative review is based on a structured literature search, with 213 publications included in the synthesis. This review synthesizes evidence on four complementary domains: pharmacogenomics, biomarkers, artificial intelligence (AI), and real-world evidence (RWE), and examines their potential integration in cardiovascular care. Pharmacogenomics has established applications in selected gene–drug pairs, including CYP2C19–clopidogrel, CYP2C9/VKORC1–warfarin, and SLCO1B1–statin-associated muscle toxicity, although implementation varies according to evidence, accessibility, and clinical infrastructure. Biomarker research extends beyond cardiac troponins to markers of myocardial stress, fibrosis, inflammation, and molecular regulation, including soluble ST2, galectin-3, GDF-15, and microRNAs. AI and machine learning provide approaches for analyzing multimodal cardiovascular data and have been investigated for risk assessment, phenotyping, imaging, ECG analysis, and clinical decision support. RWE complements randomized evidence by providing information on treatment utilization, safety, effectiveness, and outcomes in routine-care populations, while challenges include data quality, confounding, and incomplete ascertainment. Across coronary artery disease, heart failure, atrial fibrillation, hypertension, dyslipidemia, and inherited cardiovascular diseases, increasing opportunities exist to integrate these domains. However, clinical translation remains influenced by validation, interpretability, generalizability, cost, regulation, data governance, and health equity. Future research should emphasize prospective evaluation, diverse-population validation, interoperable infrastructure, and assessment of clinical utility to support the responsible integration of precision approaches into cardiovascular care.