Atrial fibrillation (AF) is a prevalent heart rhythm disorder that has serious complications including stroke, heart failure, and cognitive dysfunction. Previous genetic risk scores derived from single DNA variants have been utilized to predict AF risk. While such genetic markers are valuable, their application in the clinics is still limited due to the fact that they are inaccessible, costly, and cannot show real-time cardiac alterations.
This article highlights the novel function of artificial intelligence (AI) in improving AF prediction based on an analysis of electrocardiograms (ECGs). ECGs record the electrical activity of the heart and present a dynamic image of cardiac function, unlike the static genetic data. AI models are able to detect subtle patterns on the ECG that are more likely to lead to AF, even before the appearance of visible symptoms. These forecasting characteristics provide AI-assisted ECG interpretation with great strength as a front-end diagnostic and risk stratification tool.
By integrating AI with ECG screening, physicians can shift towards an individualized approach to AF management. The procedure is non-invasive, cost-effective, and scalable for clinical use. Being able to identify high-risk individuals before the development of AF can potentially enable earlier interventions, reducing the burden of disease and improving patient outcomes. This shift from static genetic risk assessment to dynamic, AI-driven ECG analysis is a significant advancement in precision cardiology.
Full text: Jean-Marie Grégoire, Cédric Gilon, François Marelli, Hugues Bersini, Stéphane Carlier, Genomics vs. AI-enhanced electrocardiogram: predicting atrial fibrillation in the era of precision medicine, Exploration of Digital Health Technologies, 2025;3:101141, https://doi.org/10.37349/edht.2025.101141