# Artificial Intelligence–driven Detection, Mapping, and Personalized Therapy for Atrial Fibrillation

## TL;DR

This paper reviews how artificial intelligence can improve the detection, treatment, and personalized care of atrial fibrillation, a common heart condition.

## Contribution

The paper provides a comprehensive review of AI applications in atrial fibrillation management, highlighting innovations and challenges.

## Key findings

- AI improves diagnostic yield and ablation targeting in atrial fibrillation.
- Machine learning enhances prognostic accuracy and personalization of therapy.
- Challenges include data generalizability and model interpretability in clinical settings.

## Abstract

Atrial fibrillation (AF), the most common arrhythmia worldwide, affects approximately 59 million people globally. It poses a significant health burden by increasing morbidity and mortality. Artificial intelligence (AI) is emerging as a potentially transformative technology across the AF care continuum. This review synthesizes current evidence and critically evaluates AI applications in AF management, including innovations in detection and screening using electrocardiography and wearables; advanced mapping techniques using signal processing and computational modeling to guide catheter ablation; machine learning-based prediction of treatment outcomes; and personalization of long-term therapy, such as anticoagulation. Key studies and trials illustrating AI’s capabilities in improving diagnostic yield, refining ablation targets, and enhancing prognostic accuracy are analyzed. The potential for AI to facilitate integrated care pathways, such as the “AF Better Care” approach, is considered, balancing innovation against clinical practicality, rigorous validation, and workflow integration. While AI shows considerable potential to augment precision in AF management, significant challenges concerning data generalizability, model interpretability, clinical utility validation, and equitable implementation remain. Optimal integration requires careful alignment with clinical expertise and a focus on patient-centric outcomes. Addressing these challenges through collaborative efforts among clinicians, researchers, and technology developers will be essential to fully realize AI’s promise in improving AF care. Future research should prioritize robust validation, transparent methodologies, and practical implementation strategies to ensure that AI effectively enhances patient outcomes.