A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods

A multicenter pragmatic implementation study of AI-ECG-based clinical decision support software to identify low LVEF: Clinical trial design and methods

Background: Artificial intelligence (AI) enabled algorithms can detect or predict cardiovascular conditions using electrocardiogram (ECG) data. Clinical studies have evaluated ECG-AI algorithms, including a recent single-center study which evaluated outcomes when clinicians were provided with ECG-AI...

Full description

Saved in:
Bibliographic Details
Main Authors: Francisco Lopez-Jimenez, Heather M. Alger, Zachi I. Attia, Barbara Barry, Ranee Chatterjee, Rowena Dolor, Paul A. Friedman, Stephen J. Greene, Jason Greenwood, Vinay Gundurao, Sarah Hackett, Prerak Jain, Anja Kinaszczuk, Ketan Mehta, Jason O'Grady, Ambarish Pandey, Christopher Pullins, Arjun R. Puranik, Mohan Krishna Ranganathan, David Rushlow, Mark Stampehl, Vinayak Subramanian, Kitzner Vassor, Xuan Zhu, Samir Awasthi
Format: Article
Language:English
Published: Elsevier 2025-06-01
Series:American Heart Journal Plus
Subjects:
Electrocardiogram
Artificial intelligence
Left ventricular ejection fraction
Heart failure
Clinical decision support
Best practice alerts
Online Access:http://www.sciencedirect.com/science/article/pii/S266660222500031X
Tags: Add Tag
No Tags, Be the first to tag this record!

Similar Items

Similar Items