Artificial Intelligence in Atrial Fibrillation: From Early Detection to Precision Therapy

Area of Science:

• Cardiology
• Medical Informatics
• Artificial Intelligence

Background:

• Atrial fibrillation (AF) is a common cardiac arrhythmia with substantial health impacts.
• Current AF management faces challenges in early detection, risk assessment, and treatment optimization.

Purpose of the Study:

• To review the current and emerging applications of artificial intelligence (AI) in atrial fibrillation care.
• To highlight AI's potential to enhance precision medicine and improve patient outcomes in AF management.

Main Methods:

• Review of AI applications including machine learning and deep learning algorithms.
• Analysis of AI in electrocardiographic screening, wearable monitoring, predictive modeling, and treatment guidance.
• Exploration of AI's role in anticoagulation management and catheter ablation.

Main Results:

• AI enhances AF detection through ECG screening and wearable devices.
• AI-driven models improve risk stratification and stroke prediction using diverse data.
• AI optimizes individualized AF therapies, including anticoagulation and catheter ablation strategies.

Conclusions:

• AI is a transformative tool in AF care, improving diagnostics and treatment.
• Further research on validation, interpretability, and regulation is needed for clinical AI integration.
• AI holds significant promise for advancing precision medicine and patient outcomes in atrial fibrillation.