Clinical Evidence
Our AI algorithms are backed by rigorous clinical validation, peer-reviewed publications, and real-world evidence from leading healthcare institutions.
Validated Performance
Algorithm performance validated across diverse patient populations and clinical settings.
Diastolic Dysfunction
Low Ejection Fraction
Atrial Fibrillation
Coronary Artery Disease
Hypertrophic CM
QT Prolongation
Featured Publications
Our research has been published in the world's leading cardiology journals.
AI-ECG Detection of Diastolic Dysfunction: A Multi-Center Validation Study
Chen S, Rodriguez M, Foster A, et al.
This multi-center study validated the performance of an AI-based ECG algorithm for detecting diastolic dysfunction across 12 medical centers, demonstrating 87% sensitivity and 85% specificity in a diverse patient population.
Machine Learning Approach to ECG-Based Screening for Low Ejection Fraction
Martinez J, Thompson R, Williams K, et al.
A deep learning model trained on 450,000 ECGs achieved 91% sensitivity for detecting left ventricular ejection fraction below 40%, enabling earlier identification of patients who may benefit from guideline-directed medical therapy.
Validation of AI-Enhanced ECG Interpretation in Primary Care Settings
Anderson P, Lee S, Johnson M, et al.
Prospective evaluation of AI-ECG analysis in 45 primary care clinics showed significant improvement in detection of cardiac abnormalities compared to standard ECG interpretation, with minimal impact on clinical workflow.
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