<![CDATA[In the field of cardiovascular health, machine learning and artificial intelligence (AI) have become effective tools with potential applications ranging from disease detection and diagnosis to individualized treatment planning and decision making. The purpose of this study is to identify and analyze the role of AI in cardiovascular health care. The methodology of this review paper involved an extensive literature review of the existing research on the topic of AI in cardiovascular health care. Medical imaging is very important in the diagnosis and treatment of many diseases, but the interpretation of medical images is often time-consuming and subjective. Artificial intelligence (AI) algorithms, such as supervised and unsupervised learning, have been developed to assist in the analysis and interpretation of data from medical imaging. Convolutional neural networks (CNNs) and support vector machines (SVM) are the two most frequently used AI algorithms in medical image analysis. Artificial intelligence (AI) and machine learning in cardiovascular healthcare have great potential to improve patient outcomes and lower costs. However, there are still some hurdles that need to be overcome such as integration with clinical workflows, model validation and generalization, and privacy and security issues related to patient data. To overcome this, collaboration between doctors, researchers and industrial partners is needed. This technology has a bright and promising future with continuous investment in research and development.]]>
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