@article{JMAI11269,
author = {Dimitri Chepkunov and Daye Chung and Kimia Ghanbari and Bhumika Khanna and Neri Yakubov and Myoungmee Babu and Benson Babu},
title = {Artificial intelligence-augmented electrocardiogram for heart failure diagnosis and risk prediction: a narrative review},
journal = {Journal of Medical Artificial Intelligence},
volume = {9},
number = {0},
year = {2026},
keywords = {},
abstract = {The electrocardiogram (ECG) is a widely available, low-cost diagnostic tool routinely used in cardiovascular care; however, its conventional interpretation has limited sensitivity for detecting heart failure (HF) and predicting disease progression. Artificial intelligence (AI) has emerged as a promising approach to enhance ECG interpretation by identifying latent patterns associated with cardiac dysfunction. This review summarizes the diagnostic performance and clinical utility of AI-augmented ECG models for HF diagnosis and risk prediction based on recent primary studies.},
issn = {2617-2496}, url = {https://jmai.amegroups.org/article/view/11269}
}