From Traditional to Modern: A Narrative Review of AI-Based Approaches of Cardiac Arrhythmia Diagnosis

Abstract

Cardiac arrhythmia is one of the leading causes of morbidity and mortality in the general population, and thus, early detection of arrhythmia is critical for improving patient outcomes. While the 12-lead ECG was traditionally used as the primary diagnostic tool for arrhythmia, its manual interpretation can be challenging, even for experienced cardiologists. However, with the growing understanding of cardiac arrhythmia, artificial intelligence (AI) algorithms have been developed to analyze ECGs to identify abnormalities and predict the risk of developing arrhythmia. AI can be used for real-time ECG monitoring through wearable devices to alert patients or healthcare providers if an arrhythmia is detected. It has the potential to decrease reliance on cardiologists, shorten hospital stays, and assist patients in rural hospitals with limited access to medical professionals. Although AI is known for its ability to accurately interpret large amounts of data quickly, there are concerns about its use in the medical field. Considering the crucial differences between AI and humans, we discuss the strengths and limitations of using AI to diagnose cardiac arrhythmias.

Keywords

• Artificial intelligence;
• cardiac arrythmias
• ECG
• Machine learning
• Deep learning

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