AIM The “2023 ACC/AHA/ACCP/HRS Guideline for the Diagnosis and Management of Atrial
Fibrillation” provides recommendations to guide clinicians in the treatment of patients with …

Artificial intelligence (AI), the technique of extracting information from complex database
using sophisticated computer algorithms, has incorporated itself in medical field. AI …

Artificial intelligence (AI) has been developed for echocardiography,–, although it has not yet
been tested with blinding and randomization. Here we designed a blinded, randomized non …

Early identification of atrial fibrillation (AF) can reduce the risk of stroke, heart failure, and
other serious cardiovascular outcomes. However, paroxysmal AF may not be detected even …

Summary Globally, up to 1· 5 million individuals with ischaemic stroke or transient ischaemic
attack can be newly diagnosed with atrial fibrillation per year. In the past decade, evidence …

Importance Early detection of atrial fibrillation (AF) may help prevent adverse cardiovascular
events such as stroke. Deep learning applied to electrocardiograms (ECGs) has been …

A modern-day physician is faced with a vast abundance of clinical and scientific data, by far
surpassing the capabilities of the human mind. Until the last decade, advances in data …

AF is the most common clinically relevant cardiac arrhythmia associated with multiple
comorbidities, cardiovascular complications (eg stroke) and increased mortality. As artificial …

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 Machine learning is a powerful tool that can be used to solve a wide range of
problems in various applications and industries. The healthcare sector has faced specific …