Cardiac arrhythmias are a major cause of morbidity and mortality worldwide. Conventional electrocardiogram (ECG) interpretation remains the cornerstone of arrhythmia diagnosis; however, increasing volumes of ECG data generated from hospitals, ambulatory monitors, implantable devices, and wearable technologies present significant diagnostic challenges. Artificial intelligence (AI), particularly machine learning (ML) and deep learning (DL), has emerged as a promising tool for automated arrhythmia detection.

Objective

To evaluate the diagnostic accuracy, clinical utility, and implementation challenges of AI-assisted arrhythmia detection systems in contemporary cardiology practice.

Methods

A prospective observational study was conducted involving 1,200 patients undergoing ECG monitoring. AI-based ECG analysis was compared with interpretation by experienced electrophysiologists. Outcomes included sensitivity, specificity, diagnostic accuracy, time to diagnosis, and clinical impact.

Results

AI-assisted analysis demonstrated an overall diagnostic accuracy of 96.4%. Sensitivity for atrial fibrillation detection was 98.1%, while ventricular tachycardia detection reached 95.2%. Diagnostic reporting time decreased by 72% compared with conventional interpretation. AI-assisted systems significantly improved early arrhythmia identification and reduced missed diagnoses.

Conclusion

AI-assisted arrhythmia detection provides highly accurate and efficient ECG interpretation. Integration of AI into clinical workflows may improve diagnostic speed, patient outcomes, and healthcare efficiency while complementing clinician expertise.