Artificial intelligence (AI) and machine learning (ML) based applications have been found useful in many fields of medicine. The use of the enhanced computing power of machines in clinical medicine and diagnostics has been explored in cardiovascular disease treatment. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, a newer approach to cardiovascular drug therapy and post-marketing surveys of prescription drugs. In cardiovascular medicine today, ML/AI has found a wide range of applications in cardiovascular drug therapy, pharmacogenomics, heart failure management, cardiovascular imaging, and diagnostics. AI can provide tools to apply precision medicine and big data in cardiovascular medicine, therefore augmenting the effectiveness of the cardiologist’s work. AI/ML algorithms can analyse vastly heterogeneous clinical data without any assumptions, for accurate prediction and classification.

Cardiovascular medicine can therefore benefit from the incorporation of AI. Here we have described the impact of AI in various fields of cardiovascular medicine. AI and machine learning have the potential to revolutionize the field of cardiovascular medicine. AI has found applications in the diagnosis of obstructive coronary artery disease, determination of left ventricular ejection fraction, prediction of abnormal fractional flow reserve in patients undergoing coronary computed tomography angiogram (CCTA), and readmission rates in heart failure patients.

The aim of this Special Issue is to focus on recent developments of artificial intelligence/machine learning in cardiovascular disease. Original research and review articles are welcomed.

Potential topics include but are not limited to the following:

• AI/ML in cardiovascular imaging for analysis of coronary artery disease
• AI/ML algorithm for predicting of patients with acute heart failure
• AI/ML in arrhythmias and cardiac electrophysiology
• AI/ML in mobile system for cardiovascular health management
• AI/ML exploration of molecular data in the risk prediction of cardiovascular diseases
• AI/ML in personalized cardiovascular medicine and cardiovascular imaging
• AI/ML in predict of cardiovascular complications in chronic kidney disease patients
• AI/ML in arrhythmias and cardiac electrophysiology
• AI/ML in integrating blockchain technology for cardiovascular medicine