Next-Generation Cardiovascular Imaging in Precision Medicine: Integrating Functional Imaging, Artificial Intelligence, Biomarkers, and Personalized Risk Stratification

Cardiovascular and vascular diseases remain major causes of morbidity and mortality worldwide, despite substantial advances in prevention, diagnosis, and treatment. In recent years, cardiovascular imaging has moved beyond the traditional assessment of anatomy and morphology toward a multidimensional evaluation of function, tissue composition, haemodynamics, inflammation, and individualized risk. This evolution has been driven by technological progress in echocardiography, cardiovascular magnetic resonance, computed tomography, nuclear imaging, intravascular imaging, and point-of-care ultrasound, together with the rapid development of artificial intelligence, radiomics, and predictive analytics. Advanced echocardiographic techniques, including contrast stress echocardiography and emerging methods for myocardial scar detection, may improve functional and prognostic assessment in patients with suspected or established coronary artery disease. Cardiac magnetic resonance, through tissue mapping, late gadolinium enhancement, and 4D flow imaging, provides unique information on myocardial fibrosis, perfusion, ventricular remodelling, and vascular haemodynamics. Computed tomography, particularly with the introduction of photon-counting technology, is expanding the non-invasive characterization of coronary plaques, vascular calcification, and thromboembolic disease. Hybrid imaging with PET/CT and PET/MR offers additional insight into vascular inflammation, myocardial metabolism, and active disease processes. At the same time, intravascular ultrasound, optical coherence tomography, and augmented-reality-supported imaging are refining interventional guidance, while point-of-care ultrasound is broadening access to rapid bedside cardiovascular and vascular assessment. The integration of imaging findings with circulating biomarkers, clinical scores, lipid profiles, coagulation parameters, and machine-learning models represents a promising strategy for personalized risk stratification, particularly in complex conditions such as coronary artery disease, venous thromboembolism, pulmonary embolism, and bleeding risk during antithrombotic therapy. This review summarizes current advances in cardiovascular imaging, discusses their translational implications, and highlights future directions for integrating imaging, artificial intelligence, and precision medicine into daily clinical practice.