Notice: Users may be experiencing issues with displaying some pages on stanfordhealthcare.org. We are working closely with our technical teams to resolve the issue as quickly as possible. Thank you for your patience.
 

Learn about the flu shot, COVID-19 vaccine, and our masking policy »

Stanford Health Care

Artificial Intelligence and Machine Learning in Arrhythmias and Cardiac Electrophysiology. Circulation. Arrhythmia and electrophysiology Feeny, A. K., Chung, M. K., Madabhushi, A., Attia, Z. I., Cikes, M., Firouznia, M., Friedman, P. A., Kalscheur, M. M., Kapa, S., Narayan, S. M., Noseworthy, P. A., Passman, R. S., Perez, M. V., Peters, N. S., Piccini, J. P., Tarakji, K. G., Thomas, S. A., Trayanova, N. A., Turakhia, M. P., Wang, P. J. 2020

Abstract

Artificial intelligence (AI) and machine learning (ML) in medicine are currently areas of intense exploration, showing potential to automate human tasks and even perform tasks beyond human capabilities. Literacy and understanding of AI/ML methods are becoming increasingly important to researchers and clinicians. The first objective of this review is to provide the novice reader with literacy of AI/ML methods and provide a foundation for how one might conduct an ML study. We provide a technical overview of some of the most commonly used terms, techniques, and challenges in AI/ML studies, with reference to recent studies in cardiac electrophysiology to illustrate key points. The second objective of this review is to use examples from recent literature to discuss how AI and ML are changing clinical practice and research in cardiac electrophysiology, with emphasis on disease detection and diagnosis, prediction of patient outcomes, and novel characterization of disease. The final objective is to highlight important considerations and challenges for appropriate validation, adoption, and deployment of AI technologies into clinical practice.

View details for DOI 10.1161/CIRCEP.119.007952

View details for PubMedID 32628863