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Volume 15
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10.3390/jcm15010316
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Review

AI Applications in Electrocardiography for Ischemic and Structural Heart Disease: A Review of the Current State

by
Eugene J. Kim
1,
Dhir Gala
1,
Mohammed Ayyad
1,
Manaal Pramanik
2 and
Amgad N. Makaryus
3,4,5,*
1
Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ 07003, USA
2
W. Tresper Clarke School, Salisbury, NY 11590, USA
3
Department of Cardiology, Cardiovascular Institute, Northwell Health, New Hyde Park, NY 11040, USA
4
Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
5
Plainview Hospital, Northwell Health, Plainview, NY 11803, USA
*
Author to whom correspondence should be addressed.
J. Clin. Med. 2026, 15(1), 316; https://doi.org/10.3390/jcm15010316 (registering DOI)
Submission received: 8 December 2025 / Revised: 29 December 2025 / Accepted: 29 December 2025 / Published: 1 January 2026
(This article belongs to the Topic Artificial Intelligence in Public Health: Current Trends and Future Possibilities, 2nd Edition)
Versions Notes

Abstract

Cardiovascular disease is the leading cause of morbidity and mortality worldwide, with ischemic and structural heart diseases being key contributors. While the 12-lead electrocardiogram (ECG) is a common low-cost diagnostic test, its interpretation is limited by human variability. Through machine learning with large diverse ECG data sets and artificial intelligence (AI) algorithms, ECG analysis can be automated for pattern recognition with higher accuracy. AI-augmented ECG algorithms have been demonstrated to be able to detect myocardial infarction with high accuracy and reduce door-to-balloon coronary intervention times. Similar models can be utilized to detect subtle ECG waveforms suggestive of current or future asymptomatic left ventricular dysfunction, aortic stenosis, and hypertrophic cardiomyopathy. Despite these promising results, there is concern for generalizability and bias or errors in training data. As AI systems evolve to multimodal integration, AI-augmented ECG has the potential to redefine cardiovascular diagnostics and enable earlier detection, risk stratification, and precision-guided interventions.
Keywords: artificial intelligence; AI; deep learning; electrocardiography; STEMI; NSTEMI; OMI; cardiology; hypertrophic cardiomyopathy; aortic stenosis artificial intelligence; AI; deep learning; electrocardiography; STEMI; NSTEMI; OMI; cardiology; hypertrophic cardiomyopathy; aortic stenosis

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MDPI and ACS Style

Kim, E.J.; Gala, D.; Ayyad, M.; Pramanik, M.; Makaryus, A.N. AI Applications in Electrocardiography for Ischemic and Structural Heart Disease: A Review of the Current State. J. Clin. Med. 2026, 15, 316. https://doi.org/10.3390/jcm15010316

AMA Style

Kim EJ, Gala D, Ayyad M, Pramanik M, Makaryus AN. AI Applications in Electrocardiography for Ischemic and Structural Heart Disease: A Review of the Current State. Journal of Clinical Medicine. 2026; 15(1):316. https://doi.org/10.3390/jcm15010316

Chicago/Turabian Style

Kim, Eugene J., Dhir Gala, Mohammed Ayyad, Manaal Pramanik, and Amgad N. Makaryus. 2026. "AI Applications in Electrocardiography for Ischemic and Structural Heart Disease: A Review of the Current State" Journal of Clinical Medicine 15, no. 1: 316. https://doi.org/10.3390/jcm15010316

APA Style

Kim, E. J., Gala, D., Ayyad, M., Pramanik, M., & Makaryus, A. N. (2026). AI Applications in Electrocardiography for Ischemic and Structural Heart Disease: A Review of the Current State. Journal of Clinical Medicine, 15(1), 316. https://doi.org/10.3390/jcm15010316

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