A Recent Review on Integration of Artificial Intelligence in Cardiovascular Medicine: Advancing Diagnosis, Pathophysiology, and Personalized Therapeutics
Keywords:
Cardiovascular disease (CVD), Stroke, Peripheral artery disease, Matrix metalloproteinases (MMPs), Metabolic syndrome, Hyperglycemia, Reactive oxygen species (ROS), Vascular remodeling, Plaque rupture, Thrombogenesis, Myocardial infarction, Ischemic heart diseaseAbstract
The medical field has adopted Artificial Intelligence (AI), which is the process of using advanced computer algorithms to extract information from complex databases. AI techniques have shown that they can speed up the process of diagnosing and treating cardiovascular diseases (CVDs), such as heart malfunction, atrial fibrillation, heart valve disorder, hypertrophic cardiomyopathy, also known as congenital heart disease, and more. In real-life situations, AI has been shown to work well for diagnosing CVD, making auxiliary tools more useful, classifying and typing diseases, and predicting outcomes. Advanced algorithms for artificial intelligence are expected to be able to handle even more complicated assignments than traditional methods because they are very well developed to find subtle ties in huge amounts of healthcare data. This review aims to present contemporary ways to utilize AI in cardiovascular diseases (CVDs), facilitating clinicians with limited computer science expertise to comprehend the forefront of the field and implement AI algorithms in clinical practice.
