Heart rhythm disorders (arrhythmias) represent one of the most common and dangerous forms of cardiovascular disease, often progressing asymptomatically but leading to severe complications like heart failure, stroke, or sudden death. Traditional manual ECG analysis is time-consuming and prone to missing subtle changes, prompting the integration of artificial intelligence (AI), particularly deep learning algorithms, for automated and precise detection. This article explores AI-assisted ECG analysis for early arrhythmia detection, focusing on atrial fibrillation (AF) and ventricular tachycardia (VT), and its clinical significance in reducing risks and easing physician workloads. Through an interdisciplinary review combining cardiology, data science, and evidence-based medicine, this study demonstrates how AI enhances diagnostic accuracy, enables proactive interventions, and supports holistic cardiovascular care. Understanding these advancements provides insights into prevention, risk reduction, and improved patient safety in modern clinical practice.

AI in ECG Analysis, Atrial Fibrillation Detection, Arrhythmia Early Detection, Deep Learning Algorithms, Heart Failure Prevention

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