Publications

Although artificial intelligence (AI) algorithms have been shown to be capable of identifying cardiac dysfunction, defined as ejection fraction (EF) ≤ 40%, from 12-lead electrocardiograms (ECGs), identification of cardiac dysfunction using the single-lead ECG of a smartwatch has yet to be tested. In… Read More

Published in Nature Medicine

November 14, 2022

We conducted this study to evaluate the potential utility of an AI-based cardiovascular diseases (CVD) screening tool in an under-resourced African-American cohort, we reviewed the AI-enhanced electrocardiogram (ECG) data of participants enrolled in a community-based clinical trial as a proof-of-con… Read More

Published in American Journal of Preventive Cardiology

November 13, 2022

In this study, we aimed to compare the clinicians’ characteristics of “high adopters” and “low adopters” of an artificial intelligence (AI)–enabled electrocardiogram (ECG) algorithm that alerted for possible low left ventricular ejection fraction (EF) and the subsequent effectiveness of detecting pa… Read More

Published in Mayo Clinic Proceedings

November 1, 2022

An electrocardiogram (ECG)-based artificial intelligence (AI) algorithm has shown good performance in detecting hypertrophic cardiomyopathy (HCM). However, its application in routine clinical practice may be challenging due to the low disease prevalence and potentially high false-positive rates. Thi… Read More

Published in Cardiovascular Digital Health Journal

October 22, 2022

We conducted a pragmatic study to evaluate the effectiveness of an artificial intelligence (AI) algorithm-guided targeted screening approach for identifying previously unrecognized atrial fibrillation. For this non-randomised interventional trial, we prospectively recruited patients with stroke risk… Read More

Published in The Lancet

September 27, 2022