Recent years have witnessed significant transformations in cardiovascular medicine, driven
by the rapid evolution of artificial intelligence (AI). This scoping review was conducted to …

Abstract Explainable Artificial Intelligence refers to developing artificial intelligence models
and systems that can provide clear, understandable, and transparent explanations for their …

The electrocardiogram (ECG) can capture obesity-related cardiac changes. Artificial
intelligence-enhanced ECG (AI-ECG) can identify subclinical disease. We trained an AI …

Aims This study aims to identify and visualize electrocardiogram (ECG) features using an
explainable deep learning–based algorithm to predict cardiac resynchronization therapy …

The reliable operation of power transmission networks depends on the timely detection and
localization of faults. Fault classification and localization in electricity transmission networks …

Background Risk stratification for ventricular arrhythmias currently relies on static
measurements that fail to adequately capture dynamic interactions between arrhythmic …

Aims Recent trial data demonstrate beneficial effects of active rhythm management in
patients with atrial fibrillation (AF) and support the concept that a low arrhythmia burden is …

Deep learning applications have had a profound impact on many scientific fields, including
functional genomics. Deep learning models can learn complex interactions between and …

Aims The diagnostic application of artificial intelligence (AI)-based models to detect
cardiovascular diseases from electrocardiograms (ECGs) evolves, and promising results …

Abstract The 12-lead electrocardiogram (ECG) is inexpensive and widely available. Whether
conditions across the human disease landscape can be detected using the ECG is unclear …