Causal AI in Cardiac Arrhythmia: From Pattern Recognition to Mechanistic Insight

Area of Science:

• Cardiovascular Research
• Artificial Intelligence in Medicine
• Computational Biology

Background:

• Cardiac arrhythmias are a major cause of death, and current diagnostic tools like ECG may miss transient events.
• While AI and machine learning improve arrhythmia management, most models lack mechanistic understanding, relying on statistical correlations.
• There is a need for more physiologically grounded AI approaches in cardiovascular research.

Purpose of the Study:

• To review the application of causal inference and mechanistic modeling in cardiac arrhythmia research.
• To explore how Causal AI, structural causal models, and digital twins can enhance arrhythmia detection and treatment.
• To assess the potential and limitations of these advanced AI methods in clinical practice.

Main Methods:

• A narrative review of literature from 2000-2025 was conducted using PubMed, Scopus, and Google Scholar.
• Search terms included "cardiac arrhythmia," "causal inference," "structural causal models," "digital twins," "mechanistic modeling," and "artificial intelligence."
• Studies were selected based on methodological depth, causal or mechanistic focus, and translational relevance, excluding purely predictive models.

Main Results:

• Causal AI provides a mechanistically grounded framework for understanding arrhythmogenesis and treatment effects.
• Integrating clinical data with structural causal models, mechanistic modeling, and digital twins enhances physiological interpretability.
• These approaches show potential for predicting ablation success, guiding therapy, and personalizing patient care.

Conclusions:

• Causal AI and mechanistic modeling offer significant potential for advancing cardiovascular research and clinical applications.
• Bridging the gap between predictive AI and physiological understanding is crucial for effective arrhythmia management.
• Clinical implementation is currently hindered by data variability, validation complexities, and regulatory considerations.