Abstract
Unexpected insights and practical advances in cardiovascular disease (CVD) are being discovered by rapidly advancing developments in supercomputers and machine learning (ML) software algorithms. These have been accelerated during the COVID-19 pandemic, and the resulting CVD translational implications of ML are steering new measures of prevention and treatment, new tools for objective clinical diagnosis, and even opportunities for rethinking basic foundations of CVD nosology. As the usual cardiovascular specialist may not be familiar with these tools, the editor has invited this brief overview.