Background

Electrocardiography (ECG) remains one of the most widely used diagnostic tools for assessing cardiac electrical activity and detecting cardiovascular abnormalities. However, ECG interpretation often requires specialized expertise and is susceptible to human variability and diagnostic errors. Recent advances in Artificial Intelligence (AI), particularly machine learning and deep learning algorithms, have significantly enhanced automated ECG interpretation capabilities.

Objective

This study examines the role of Artificial Intelligence in ECG interpretation, evaluates current AI methodologies, explores clinical applications, and discusses challenges and future opportunities in AI-assisted cardiac diagnostics.

Methodology

A narrative review and conceptual analysis were conducted using published literature from cardiology, biomedical engineering, and artificial intelligence domains. Various machine learning architectures, clinical applications, and implementation frameworks were examined.

Results

AI-based ECG interpretation systems demonstrate high accuracy in detecting arrhythmias, myocardial infarction, atrial fibrillation, heart failure, and other cardiovascular abnormalities. Deep learning models have achieved diagnostic performance comparable to expert cardiologists in several clinical settings.

Conclusion

Artificial Intelligence is revolutionizing ECG interpretation by improving diagnostic efficiency, accuracy, and accessibility. Despite challenges related to explainability, regulatory compliance, and data quality, AI has the potential to become an integral component of future cardiovascular care.