Artificial Intelligence in Cardiac Electrophysiology: Enhancing Mapping and Ablation Precision

Area of Science:

• Cardiovascular Medicine
• Medical Informatics
• Artificial Intelligence

Background:

• Artificial intelligence (AI) is increasingly impacting cardiac electrophysiology (EP).
• Current EP workflows face challenges in arrhythmia detection, procedural planning, and outcome prediction.
• AI offers potential solutions to enhance diagnostic accuracy and treatment efficacy.

Purpose of the Study:

• To review recent advancements in AI applications within cardiac EP.
• To evaluate the validation, clinical performance, and implementation hurdles of AI tools in EP.
• To discuss future directions and ethical considerations for AI in EP.

Main Methods:

• Systematic review of recent literature on AI in cardiac electrophysiology.
• Analysis of AI model validation strategies, including internal, external, and multicenter approaches.
• Assessment of clinical performance metrics and implementation challenges.

Main Results:

• AI tools show promise in improving arrhythmia detection, procedural planning, and outcome prediction.
• External and multicenter validation are crucial for generalizability of AI models.
• AI applications have demonstrated potential to reduce ablation times, fluoroscopy, and recurrence rates.
• Clinical integration is hindered by data bias, interpretability issues, and workflow integration challenges.

Conclusions:

• AI holds significant potential to improve clinical decision-making and patient outcomes in EP.
• Addressing technical, regulatory, and ethical barriers is essential for widespread AI adoption in EP.
• Future innovations like explainable AI and digital twins require further validation for clinical implementation.