ARTIFICIAL INTELLIGENCE IN CARDIAC IMAGING: INNOVATIONS AND FUTURE DIRECTIONS

Abstract

Artificial Intelligence (AI) has emerged as a transformative tool in cardiac imaging, offering significant advancements in the diagnosis, prognosis, and management of cardiovascular diseases. This article investigates the incorporation of artificial intelligence (AI) technologies, such as machine learning and deep learning, into a variety of imaging modalities, such as nuclear imaging, computed tomography (CT), cardiac magnetic resonance imaging (MRI), and echocardiography. By automating image acquisition, interpretation, and analysis, Artificial intelligence has the potential to improve diagnosis accuracy, decrease the variability that occurs across observers, and streamline clinical operations. Key innovations include AI-driven segmentation for cardiac structures, automated quantification of myocardial function, and risk stratification through predictive analytics. Despite these advancements, challenges remain in the form of ethical considerations, data privacy, algorithm validation, and the need for clinical standardization. This review examines the current state of AI in cardiac imaging, highlights its clinical applications, and discusses future directions for research and implementation, emphasizing the role of AI in shaping a new era of precision cardiology.