ARTIFICIAL INTELLIGENCE IN CARDIAC IMAGING

Abstract

Artificial Intelligence in Cardiac Imaging is transforming the way cardiovascular diseases are detected, diagnosed, and managed. In recent years, AI has become an important part of modern cardiac imaging by helping clinicians analyse images more quickly and accurately. Technologies such as machine learning, deep learning, and convolutional neural networks are now widely used in imaging techniques including echocardiography, cardiac magnetic resonance imaging (MRI), computed tomography (CT), and nuclear cardiology imaging such as PET and SPECT. These AI-based systems help in image reconstruction, automated measurements, motion correction, and functional assessment of the heart, improving both diagnostic accuracy and workflow efficiency. AI also plays a valuable role in clinical decision-making by supporting risk assessment and personalized treatment planning. By reducing manual workload and minimizing observer variability, AI has the potential to improve patient care and early detection of cardiovascular diseases. However, several challenges still remain, including concerns related to data privacy, limited availability of high-quality datasets, algorithmic bias, and the need for proper ethical and regulatory guidelines. New developments such as explainable AI, federated learning, and integration with electronic health records may help address these issues in the future. Overall, Artificial Intelligence in Cardiac Imaging represents a promising advancement in cardiovascular medicine, but its successful implementation will depend on continued collaboration between healthcare professionals, researchers, engineers, and policymakers.