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

Cardiac arrhythmias remain a leading cause of cardiovascular morbidity and mortality worldwide, and conventional diagnostic tools such as electrocardiography and Holter monitoring may fail to detect transient or asymptomatic events. Recent advances in artificial intelligence (AI) and machine learning have enhanced arrhythmia detection, risk stratification, and treatment planning; however, most existing models rely primarily on statistical associations rather than underlying physiological mechanisms.

This narrative review was conducted through a structured but non-systematic literature search of PubMed, Scopus, and Google Scholar, covering studies published between 2000 and 2025. Search terms included combinations of "cardiac arrhythmia," "atrial fibrillation," "causal inference," "structural causal models," "digital twins," "mechanistic modeling," "cardiac electrophysiology modeling," and "artificial intelligence." Peer-reviewed articles were included if they demonstrated methodological depth and addressed causal inference or mechanistic modeling approaches in cardiovascular research, particularly in arrhythmia detection, risk prediction, treatment optimization, or clinical validation frameworks. Studies were prioritized based on methodological rigor, translational relevance, and recency. Editorials lacking methodological detail, non-English publications, and studies relying solely on predictive models without incorporating causal or mechanistic components were excluded.

Causal artificial intelligence (Causal AI), offers a more mechanistically grounded framework for understanding arrhythmogenesis and therapeutic outcomes. Emerging evidence suggests that integrating clinical data with structural causal models, mechanistic modeling, and patient-specific digital twins can bridge the gap between predictive performance and physiological interpretability. These approaches show promise in predicting ablation success, guiding therapy, and improving individualized care.

Despite this potential, clinical implementation remains limited due to data heterogeneity, validation challenges, and regulatory constraints.