Artificial Intelligence-enabled Electrocardiography for Early Detection of Silent Atrial Fibrillation: Advances, Challenges, and Future Perspectives

Background Silent atrial fibrillation is a common problem that often goes undetected, owing to its asymptomatic presentation. Electrocardiography is utilized to diagnose subclinical atrial fibrillation; however, it is limited by its reliance on human interpretation. To overcome this limitation, artificial intelligence has recently been integrated into the conventional electrocardiogram, facilitating earlier and more precise detection. Objective This review analytically discusses the current literature associated with the advantages and drawbacks of artificial intelligence in electrocardiography, providing insight into the algorithms, practical applications, and overall effectiveness as an alternative to the standard 12-lead electrocardiogram. Results Artificial intelligence-enhanced electrocardiography has displayed consistently promising results from its high sensitivity and specificity in detecting asymptomatic atrial fibrillation, as evidenced by its clinical validation and real-world applications. Neural network-based models detecting subtle waveform abnormalities have shown a more accurate interpretation of an electrocardiogram than a typical physician and potential integration with wearable devices to support real-time monitoring. However, significant barriers remain, including systemic algorithmic bias related to age, sex, and race, as well as certain technical limitations. Conclusion The use of artificial intelligence in electrocardiography demonstrates a strong potential for improving silent atrial fibrillation detection and providing timely intervention. While artificial intelligence is highly efficient for early intervention, it is currently best utilized as a clinical addition rather than a replacement for traditional care. Finally, the clinical utility of these models needs to be solidified by prospective randomized controlled trials to further establish their impact.