ABSTRACT
The integration of artificial intelligence (AI) in cardiology has significantly transformed diagnostic and preventive strategies in cardiovascular medicine. In 2024, advancements in machine learning and deep learning have enabled the processing of multimodal clinical data, including imaging, electrocardiograms, genomics, and wearable device outputs. This article reviews the latest research on the application of AI in early detection, risk stratification, and personalized prevention of cardiovascular diseases (CVD). Emphasis is placed on AI-enhanced echocardiography, cardiac MRI, telemedicine, and continuous patient monitoring. Additionally, the development of adaptive and federated learning models ensures improved accuracy, data privacy, and real-time clinical applicability. Finally, the integration of socio-economic and environmental factors into AI models marks a significant shift toward holistic and equitable cardiovascular care. The article outlines the current capabilities, future directions, and ongoing challenges of implementing AI in clinical cardiology.
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