Artificial intelligence and atrial fibrillation: who has the winning rhythm?

Cardiology and Vascular Medicine

By Ana Espino | Published on July 15, 2025 | 3 min read

#AtrialFibrillation #AI #Diagnosis

Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide, affecting millions and significantly increasing the risk of stroke, heart failure, and mortality. Despite therapeutic advances, its management still faces several limitations: delayed diagnosis, heterogeneous clinical presentations, variable treatment responses, and challenges in risk stratification.

In this context, artificial intelligence (AI) offers a promising outlook. With its capacity to analyze massive datasets, it could transform how AF is screened, diagnosed, predicted, and treated. However, many challenges still hinder its integration into clinical practice, including algorithmic bias, lack of model interpretability, and data heterogeneity.

This review explores the current state of AI in AF management, identifying areas of application, potential benefits, and barriers to clinical implementation.

Can AI really outperform cardiologists?

AI is now being applied at several key stages in the management of atrial fibrillation. In early screening, convolutional neural networks applied to standard electrocardiograms (ECGs) can detect episodes of AF—even in patients in sinus rhythm—with accuracy rates reaching 80–90% in some studies. For risk stratification, predictive models incorporating clinical, electrophysiological, or imaging data sometimes outperform conventional scores like CHA2DS2-VASc in anticipating strokes, recurrences, or disease progression.

In the field of mapping and ablation procedures, AI—integrated with 3D navigation systems—improves detection of arrhythmogenic zones, allowing more targeted and potentially more effective interventions. Finally, remote monitoring has seen substantial growth, driven by wearable devices and AI-powered smartwatches, offering real-time, precise, and personalized heart rhythm tracking and laying the foundation for truly predictive medicine.

Despite these promising advances, the reported results remain highly heterogeneous, influenced by cohort variability, differences in algorithms used, and the quality of the available data. Furthermore, very few models have undergone external validation or been tested in randomized clinical trials, limiting their widespread clinical adoption for now.

Read next: Objectify, predict, personalize: the AI revolution in organ transplantation

AI and AF: a new era or just a flutter?

Atrial fibrillation is a complex, multifactorial, and evolving disease that continues to challenge modern medicine with its clinical variability and severe complications. In the face of these challenges, artificial intelligence provides innovative tools that can improve diagnostic accuracy, refine risk stratification, and optimize therapeutic interventions.

The goal of this review was to map the current state of AI in AF management, highlighting both its promises and its limitations. While the results are encouraging, most algorithms still lack robustness, transparency, and clinical validation. Moving forward requires the development of explainable models, tested in real-life contexts with reliable data, and the training of healthcare professionals to use them wisely, thus promoting safe and effective integration into cardiovascular care.

Read next: Oncology: When AI Takes Control…

About the author – Ana Espino

PhD in Immunology, specialized in Virology

As a scientific writer, Ana is passionate about bridging the gap between research and real-world impact. With expertise in immunology, virology, oncology, and clinical studies, she makes complex science clear and accessible. Her mission: to accelerate knowledge sharing and empower evidence-based decisions through impactful communication.

Source(s) :
Antoun, I., et al. (2025). Artificial intelligence in atrial fibrillation: emerging applications, research directions and ethical considerations. Frontiers in cardiovascular medicine, 12, 1596574 ;