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Updated: Aug 5, 2026
28:13
Catheter Ablation in Combination With Left Atrial Appendage Closure for Atrial Fibrillation
Published on: February 26, 2013
AI-Driven Atrial Fibrillation Management: From Signal to Strategy
Vedat Cicek1, Mert Ilker Hayiroglu2, Vanshali Sharma1
1Department of Radiology, Machine and Hybrid Intelligence Lab, Northwestern University, Illinois, United States.
Balkan Medical Journal
|July 30, 2026
Summary
Artificial intelligence (AI) offers new ways to detect atrial fibrillation (AF), a common heart rhythm disorder. AI tools improve diagnosis, risk prediction, and treatment planning for AF patients.
Area of Science:
- Cardiology
- Medical Informatics
- Artificial Intelligence
Background:
- Atrial fibrillation (AF) is a prevalent cardiac arrhythmia linked to significant health issues like stroke and heart failure.
- Current diagnostic methods struggle with detecting intermittent or asymptomatic AF, leading to underdiagnosis and suboptimal management.
- Artificial intelligence (AI) presents a transformative approach to enhancing AF detection and care.
Purpose of the Study:
- To provide a comprehensive review of current AI applications in atrial fibrillation management.
- To highlight AI's role in early detection, risk stratification, imaging, decision support, and interventional electrophysiology for AF.
- To discuss the challenges and future directions for AI in AF care.
Main Methods:
- Review of current literature on AI applications in atrial fibrillation.
- Analysis of AI-based electrocardiographic analysis, wearable technologies, and multimodal data integration.
- Examination of AI's impact on cardiovascular imaging and interventional procedures.
Main Results:
- AI demonstrates promise in detecting subclinical AF through electrocardiography and wearables, aiding population screening.
- Multimodal AI models improve accuracy in predicting stroke risk, recurrence, and guiding therapeutic strategies.
- AI enhances atrial segmentation, fibrosis characterization, ablation planning, and procedural efficiency in electrophysiology.
Conclusions:
- AI offers significant advancements in early detection, risk assessment, and personalized treatment for atrial fibrillation.
- Challenges such as validation, data issues, interpretability, and clinical integration need addressing for widespread AI adoption.
- Future AI development should focus on explainable, validated, and human-centered systems integrated into clinical workflows for optimal AF management.
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