Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram
By Z. Attia, S. Kapa, F. Lopez‐Jimenez, P. McKie, Dorothy J. Ladewig, Gaurav Satam, Patricia A. Pellikka, M. Enriquez-Sarano, P. Noseworthy, T. Munger and 4 more. Nature Medicine, 2019
1,188 citations recorded by the source corpus.
Subfields: Cardiology and Cardiovascular Medicine
View Paper: https://www.semanticscholar.org/paper/b9e51b968d8c341771bf6023792118a264e214a9
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In Cardiology and Cardiovascular Medicine
Citations in the corpus
- The Recent Progress and Applications of Digital Technologies in Healthcare: A Review (2020, methodology): “For example, Attia et al. studied the possibility of the application of AI to the electrocardiogram (ECG), a routine method of measuring the heart ’ s electrical activity, for the diagnosis of Asymptomatic left ventricular dysfunction (ALVD) [32].”
- Artificial intelligence and machine learning in precision and genomic medicine (2021, neutral): “[164].”
- Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial (2021, methodology): “We employed the same algorithm as was reported previously 17 .”
- Artificial intelligence in clinical and genomic diagnostics (2019, background): “tractile dysfunction [24], and blood chemistries linked to cardiac rhythm abnormalities [25].”