Artificial Intelligence in Cardiovascular Imaging: Current Applications and New Horizons

Artificial intelligence (AI) is transforming cardiovascular imaging (CVI), enhancing accuracy, efficiency, and diagnostic capability across echocardiography (Echo), cardiac computed tomography (CCT), and cardiac magnetic resonance (CMR). In Echo, AI improves image acquisition, segmentation, quantification of chamber function, and detection of wall motion abnormalities, supporting diagnosis and prognosis in various diseases. Automated two-dimensional and three-dimensional (3D) analysis allows rapid, reproducible assessments of ventricular volumes and EF. In valvular heart disease, AI assists in measurement, procedural planning, and integration with 3D printing. CCT benefits from AI at every workflow stage, from image acquisition to disease assessment. AI optimizes scanning protocols, reduces radiation exposure, and enhances coronary artery calcium scoring, plaque analysis, and ischemia evaluation. Algorithms enable rapid segmentation and functional assessment, while ongoing studies support its utility in risk prediction and plaque characterization. In CMR, AI accelerates acquisition, reduces artifacts, and automates segmentation and tissue characterization. Deep learning (DL) models accurately detect fibrosis, scar, and functional parameters, positively influencing prognosis prediction in every cardiac disease. AI-driven tools also streamline report generation, enhance Telemedicine workflow, and guide less experienced users in image acquisition. Despite these advances, challenges remain. Robust and diverse datasets, explainable AI models, regulatory approvals, and ethical considerations are critical for safe and widespread adoption. AI's 'black box' nature hinders clinician trust, making interpretability essential. As these barriers are addressed, AI is expected to become an essential tool in every aspect of CVI, enabling personalized medicine, improving patient care, and optimizing clinical workflows in the coming decades.