Closed-Loop AI Systems for Arrhythmia Management: Toward Predictive, Personalized, and Safe Cardiac Care.

BACKGROUND: Cardiac arrhythmias impose a substantial global health and economic burden. Despite advances in electrophysiology and device-based therapy, management still depends on episodic monitoring and delayed decision-making, leaving a persistent gap between detection and intervention that is widest for intermittent and asymptomatic rhythm disturbances. OBJECTIVE: This narrative review traces the shift from open-loop arrhythmia care to closed-loop, AI-assisted systems that couple continuous sensing, automated decision-making, and therapeutic actuation within a feedback-controlled architecture. Its contribution is to reframe arrhythmia AI around the control loop and its safety constraints rather than around detection accuracy alone, addressing a gap left by reviews that focus on classification algorithms or digital-twin modeling. METHODS: We synthesized evidence from landmark device-programming trials, foundational work in control theory and machine learning, and current regulatory guidance to define the principles, technological basis, and system-level requirements of closed-loop arrhythmia care. As a narrative review, it emphasizes control logic, safety constraints, feedback, and clinical integration rather than algorithmic performance in isolation. RESULTS: Device-programming trials show that conservative timing, hierarchical escalation, and defined safety boundaries reduce inappropriate therapy and can improve survival, establishing the control principles on which AI systems must build. Machine learning extends these principles to scalable monitoring and adaptive decision-making but introduces concerns of stability, transparency, bias, and performance drift. Safe implementation therefore requires dependable sensing, bounded adaptation, supervisory control, and lifecycle evaluation. CONCLUSIONS: Closed-loop AI extends established electrophysiologic control principles rather than replacing clinical judgment. Embedded within transparent, governed architectures with bounded adaptation and human oversight, such systems may improve the personalization, safety, and outcomes of arrhythmia care.