Narayan, Sanjiv M.

Use of artificial intelligence in improving outcomes in heart disease: a scientific statement from the American Heart Association

Armoundas, A. A., Narayan, S. M., Arnett, D. K., Spector-Bagdady, K., Bennett, D. A., Celi, L. A., Friedman, P. A., Gollob, M. H., Hall, J. L., Kwitek, A. E., et al. (2024) · Circulation

Artificial Intelligence in Healthcare and EducationArtificial Intelligence in Healthcare

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Integrating blockchain technology with artificial intelligence for cardiovascular medicine

Krittanawong, C., Rogers, A. J., Aydar, M., Choi, E., Johnson, K. W., Wang, Z., & Narayan, S. M. (2020) · Nature Reviews Cardiology

Blockchain Technology Applications and SecurityArtificial Intelligence in Healthcare and Education

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Race, Sex, and Age Disparities in the Performance of ECG Deep Learning Models Predicting Heart Failure

Kaur, D., Hughes, J. W., Rogers, A. J., Kang, G., Narayan, S. M., Ashley, E. A., & Perez, M. V. (2024) · Circulation: Heart Failure

Artificial Intelligence in HealthcareECG Monitoring and Analysis

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Multimodal explainable artificial intelligence identifies patients with non-ischaemic cardiomyopathy at risk of lethal ventricular arrhythmias

Kolk, M. Z. H., Ruipérez-Campillo, S., Allaart, C. P., Wilde, A. A. M., Knops, R. E., Narayan, S. M., Tjong, F. V. Y., DEEP RISK investigators, Raijmakers, F. D., Van Der Lingen, A.-L. C. J., et al. (2024) · Scientific Reports

Cardiac electrophysiology and arrhythmiasECG Monitoring and Analysis

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Racial, ethnic, and sex disparities in atrial fibrillation management: rate and rhythm control

Gomez, S. E., Fazal, M., Nunes, J. C., Shah, S., Perino, A. C., Narayan, S. M., Tamirisa, K. P., Han, J. K., Rodriguez, F., & Baykaner, T. (2022) · Journal of Interventional Cardiac Electrophysiology

Atrial Fibrillation Management and OutcomesCardiac Arrhythmias and Treatments

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