%0 Journal Article
%T Artificial intelligence-augmented electrocardiogram for heart failure diagnosis and risk prediction: a narrative review
%A Chepkunov, Dimitri
%A Chung, Daye
%A Ghanbari, Kimia
%A Khanna, Bhumika
%A Yakubov, Neri
%A Babu, Myoungmee
%A Babu, Benson
%J Journal of Medical Artificial Intelligence
%D 2026
%B 2026
%9 
%! Artificial intelligence-augmented electrocardiogram for heart failure diagnosis and risk prediction: a narrative review
%K 
%X 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.
%U https://jmai.amegroups.org/article/view/11269
%V 9
%P 
%@ 2617-2496