Artificial Intelligence in Cardiac Electrophysiology: A Comprehensive Review

Area of Science:

• Cardiology
• Medical Technology
• Artificial Intelligence

Background:

• Artificial Intelligence (AI) is increasingly integrated into cardiology, particularly electrophysiology.
• AI's capacity for analyzing complex datasets is transforming diagnostic and therapeutic strategies.
• Electrophysiology is rapidly adopting AI for enhanced patient care.

Purpose of the Study:

• To provide a comprehensive review of AI models and applications in cardiac electrophysiology.
• To examine AI's role in ECG interpretation, arrhythmia detection, atrial mapping, and catheter ablation.
• To explore the limitations and future potential of AI in electrophysiology.

Main Methods:

• Review of relevant literature on AI, Machine Learning (ML), and Deep Learning (DL) in electrophysiology.
• Thematic examination of key clinical applications of AI.
• Narrative synthesis of current AI capabilities, technologies, and outcomes in electrophysiology.

Main Results:

• AI tools effectively identify various arrhythmias, including atrial fibrillation (AF), atrial flutter (AFL), ventricular tachycardias (VTs), and long QT syndrome (LQTS).
• AI improves electro-anatomical mapping, reduces operative times, and aids in post-ablation management.
• AI demonstrates significant potential in enhancing diagnostic accuracy and procedural efficiency.

Conclusions:

• AI presents significant advantages in diagnosing arrhythmias and improving procedural efficiency in electrophysiology.
• Challenges such as data security, ethical transparency, and clinical adoption need to be addressed.
• Overcoming these limitations is key to integrating AI into routine electrophysiology practice and maximizing its future impact.