Attia, Zachi I.

An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction

Attia, Z. I., Noseworthy, P. A., Lopez-Jimenez, F., Asirvatham, S. J., Deshmukh, A. J., Gersh, B. J., Carter, R. E., Yao, X., Rabinstein, A. A., Erickson, B. J., et al. (2019) · The Lancet

ECG Monitoring and AnalysisAtrial Fibrillation Management and Outcomes

Cited by 1,613

Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram

Attia, Z. I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D. J., Satam, G., Pellikka, P. A., Enriquez-Sarano, M., Noseworthy, P. A., Munger, T. M., et al. (2019) · Nature Medicine

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 1,389

Artificial intelligence-enhanced electrocardiography in cardiovascular disease management

Siontis, K. C., Noseworthy, P. A., Attia, Z. I., & Friedman, P. A. (2021) · Nature Reviews Cardiology

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare and Education

Cited by 914

Artificial intelligence–enabled electrocardiograms for identification of patients with low ejection fraction: a pragmatic, randomized clinical trial

Yao, X., Rushlow, D. R., Inselman, J. W., McCoy, R. G., Thacher, T. D., Behnken, E. M., Bernard, M. E., Rosas, S. L., Akfaly, A., Misra, A., et al. (2021) · Nature Medicine

Artificial Intelligence in Healthcare and EducationECG Monitoring and Analysis

Cited by 424

Detection of hypertrophic cardiomyopathy using a convolutional neural network-enabled electrocardiogram

Ko, W.-Y., Siontis, K. C., Attia, Z. I., Carter, R. E., Kapa, S., Ommen, S. R., Demuth, S. J., Ackerman, M. J., Gersh, B. J., Arruda-Olson, A. M., et al. (2020) · Journal of the American College of Cardiology

ECG Monitoring and AnalysisCardiac electrophysiology and arrhythmias

Cited by 404

Development and Validation of a Deep-Learning Model to Screen for Hyperkalemia From the Electrocardiogram

Galloway, C. D., Valys, A. V., Shreibati, J. B., Treiman, D. L., Petterson, F. L., Gundotra, V. P., Albert, D. E., Attia, Z. I., Carter, R. E., Asirvatham, S. J., et al. (2019) · JAMA Cardiology

ECG Monitoring and AnalysisPotassium and Related Disorders

Cited by 318

Artificial intelligence-guided screening for atrial fibrillation using electrocardiogram during sinus rhythm: a prospective non-randomised interventional trial

Noseworthy, P. A., Attia, Z. I., Behnken, E. M., Giblon, R. E., Bews, K. A., Liu, S., Gosse, T. A., Linn, Z. D., Deng, Y., Yin, J., et al. (2022) · The Lancet

Atrial Fibrillation Management and OutcomesECG Monitoring and Analysis

Cited by 257

Electrocardiogram screening for aortic valve stenosis using artificial intelligence

Cohen-Shelly, M., Attia, Z. I., Friedman, P. A., Ito, S., Essayagh, B. A., Ko, W.-Y., Murphree, D. H., Michelena, H. I., Enriquez-Sarano, M., Carter, R. E., et al. (2021) · European Heart Journal

Cardiac Valve Diseases and TreatmentsECG Monitoring and Analysis

Cited by 252

Assessing and Mitigating Bias in Medical Artificial Intelligence: The Effects of Race and Ethnicity on a Deep Learning Model for ECG Analysis

Noseworthy, P. A., Attia, Z. I., Brewer, L. C., Hayes, S. N., Yao, X., Kapa, S., Friedman, P. A., & Lopez-Jimenez, F. (2020) · Circulation: Arrhythmia and Electrophysiology

Artificial Intelligence in Healthcare and EducationECG Monitoring and Analysis

Cited by 220

Artificial Intelligence-Enabled ECG Algorithm to Identify Patients With Left Ventricular Systolic Dysfunction Presenting to the Emergency Department with Dyspnea

Adedinsewo, D., Carter, R. E., Attia, Z., Johnson, P., Kashou, A. H., Dugan, J. L., Albus, M., Sheele, J. M., Bellolio, F., Friedman, P. A., et al. (2020) · Circulation: Arrhythmia and Electrophysiology

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 169

Artificial Intelligence–Enhanced Electrocardiogram for the Early Detection of Cardiac Amyloidosis

Grogan, M., Lopez-Jimenez, F., Cohen-Shelly, M., Dispenzieri, A., Attia, Z. I., Abou Ezzedine, O. F., Lin, G., Kapa, S., Borgeson, D. D., Friedman, P. A., et al. (2021) · Mayo Clinic Proceedings

Amyloidosis: Diagnosis, Treatment, OutcomesECG Monitoring and Analysis

Cited by 166

Prospective evaluation of smartwatch-enabled detection of left ventricular dysfunction

Attia, Z. I., Harmon, D. M., Dugan, J., Manka, L., Lopez-Jimenez, F., Lerman, A., Siontis, K. C., Noseworthy, P. A., Yao, X., Klavetter, E. W., et al. (2022) · Nature Medicine

ECG Monitoring and AnalysisNon-Invasive Vital Sign Monitoring

Cited by 154

Artificial Intelligence-Enabled ECG for Left Ventricular Diastolic Function and Filling Pressure

Lee, E., Ito, S., Miranda, W. R., Lopez-Jimenez, F., Kane, G. C., Asirvatham, S. J., Noseworthy, P. A., Friedman, P. A., Carter, R. E., Borlaug, B. A., et al. (2024) · npj Digital Medicine

ECG Monitoring and AnalysisCardiovascular Function and Risk Factors

Cited by 106

ECG AI-Guided Screening for Low Ejection Fraction (EAGLE): Rationale and design of a pragmatic cluster randomized trial

Yao, X., McCoy, R. G., Friedman, P. A., Shah, N. D., Barry, B. A., Behnken, E. M., Inselman, J. W., Attia, Z. I., & Noseworthy, P. A. (2020) · American Heart Journal

Artificial Intelligence in Healthcare and EducationECG Monitoring and Analysis

Cited by 98

Artificial intelligence guided screening for cardiomyopathies in an obstetric population: a pragmatic randomized clinical trial

Adedinsewo, D. A., Morales-Lara, A. C., Afolabi, B. B., Kushimo, O. A., Mbakwem, A. C., Ibiyemi, K. F., Ogunmodede, J. A., Raji, H. O., Ringim, S. H., Habib, A. A., et al. (2024) · Nature Medicine

Cardiovascular Issues in PregnancyArtificial Intelligence in Healthcare and Education

Cited by 63

Artificial intelligence electrocardiogram as a novel screening tool to detect a newly abnormal left ventricular ejection fraction after anthracycline-based cancer therapy

Jacobs, J. E. J., Greason, G., Mangold, K. E., Wildiers, H., Willems, R., Janssens, S., Noseworthy, P., Lopez-Jimenez, F., Voigt, J.-U., Friedman, P., et al. (2024) · European Journal of Preventive Cardiology

Chemotherapy-induced cardiotoxicity and mitigationECG Monitoring and Analysis

Cited by 49

Postdevelopment Performance and Validation of the Artificial Intelligence-Enhanced Electrocardiogram for Detection of Cardiac Amyloidosis

Harmon, D. M., Mangold, K., Baez Suarez, A., Scott, C. G., Murphree, D. H., Malik, A., Attia, Z. I., Lopez-Jimenez, F., Friedman, P. A., Dispenzieri, A., et al. (2023) · JACC: Advances

Amyloidosis: Diagnosis, Treatment, OutcomesECG Monitoring and Analysis

Cited by 47

Automated detection of low ejection fraction from a one-lead electrocardiogram: application of an AI algorithm to an electrocardiogram-enabled Digital Stethoscope

Attia, Z. I., Dugan, J., Rideout, A., Maidens, J. N., Venkatraman, S., Guo, L., Noseworthy, P. A., Pellikka, P. A., Pham, S. L., Kapa, S., et al. (2022) · European Heart Journal - Digital Health

ECG Monitoring and AnalysisPhonocardiography and Auscultation Techniques

Cited by 46

Hypertrophic cardiomyopathy detection with artificial intelligence electrocardiography in international cohorts: an external validation study

Siontis, K. C., Wieczorek, M. A., Maanja, M., Hodge, D. O., Kim, H.-K., Lee, H.-J., Lee, H., Lim, J., Park, C. S., Ariga, R., et al. (2024) · European Heart Journal - Digital Health

ECG Monitoring and AnalysisCardiomyopathy and Myosin Studies

Cited by 44

Artificial intelligence and the electrocardiogram: A modern renaissance

Palermi, S., Vecchiato, M., Ng, F. S., Attia, Z., Cho, Y., Anselmino, M., De Ferrari, G. M., Saglietto, A., Sau, A., Chiu, I.-M., et al. (2025) · European Journal of Internal Medicine

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare and Education

Cited by 42

Assessment of disease status and treatment response with artificial Intelligence- Enhanced electrocardiography in obstructive hypertrophic cardiomyopathy

Tison, G. H., Siontis, K. C., Abreau, S., Attia, Z., Agarwal, P., Balasubramanyam, A., Li, Y., Sehnert, A. J., Edelberg, J. M., Friedman, P. A., et al. (2022) · Journal of the American College of Cardiology

Cardiomyopathy and Myosin StudiesECG Monitoring and Analysis

Cited by 42

An electrocardiogram-based AI algorithm for early detection of pulmonary hypertension

DuBrock, H. M., Wagner, T. E., Carlson, K., Carpenter, C. L., Awasthi, S., Attia, Z. I., Frantz, R. P., Friedman, P. A., Kapa, S., Annis, J., et al. (2024) · European Respiratory Journal

ECG Monitoring and AnalysisPulmonary Hypertension Research and Treatments

Cited by 38

Saliency Maps Provide Insights into Artificial Intelligence-Based Electrocardiography Models for Detecting Hypertrophic Cardiomyopathy

Siontis, K. C., Suárez, A. B., Sehrawat, O., Ackerman, M. J., Attia, Z. I., Friedman, P. A., Noseworthy, P. A., & Maanja, M. (2023) · Journal of Electrocardiology

ECG Monitoring and AnalysisCardiac electrophysiology and arrhythmias

Cited by 38

Real-World Performance, Long-Term Efficacy, and Absence of Bias in the Artificial Intelligence Enhanced Electrocardiogram to Detect Left Ventricular Systolic Dysfunction

Harmon, D. M., Carter, R. E., Cohen-Shelly, M., Svatikova, A., Adedinsewo, D. A., Noseworthy, P. A., Kapa, S., Lopez-Jimenez, F., Friedman, P. A., & Attia, Z. I. (2022) · European Heart Journal - Digital Health

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare and Education

Cited by 35