Background

Electrocardiography (ECG) remains one of the most widely used diagnostic tools in cardiovascular medicine. However, accurate ECG interpretation requires considerable expertise and experience. Recent advances in Artificial Intelligence (AI), particularly machine learning (ML) and deep learning (DL), have revolutionized ECG analysis by enabling automated detection, classification, and prediction of cardiovascular abnormalities.

Objective

This study evaluates the role of artificial intelligence in electrocardiogram interpretation, its clinical applications, diagnostic performance, benefits, challenges, and future prospects in cardiovascular healthcare.

Methods

A narrative review and analytical assessment of peer-reviewed literature published between 2018 and 2025 were conducted. Studies evaluating AI-assisted ECG interpretation, arrhythmia detection, myocardial infarction diagnosis, heart failure prediction, and predictive analytics were reviewed.

Results

AI systems demonstrated diagnostic accuracies exceeding 90% for many ECG-based conditions, including atrial fibrillation, myocardial infarction, ventricular arrhythmias, and heart failure prediction. Deep learning models consistently outperformed conventional algorithms and showed diagnostic performance comparable to expert cardiologists in several studies.

Conclusion

Artificial intelligence has emerged as a powerful tool for ECG interpretation, improving diagnostic accuracy, workflow efficiency, and early disease detection. Future integration of AI into routine clinical practice may significantly enhance cardiovascular care and patient outcomes.