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Atrial fibrillation (AFib) is a major and often undiagnosed risk factor for ischemic stroke, with paroxysmal episodes that frequently evade conventional intermittent monitoring. Wearable devices combining photoplethysmography (PPG) and single-lead ECG have enabled large-scale AFib screening, but many current systems rely on cloud-based processing, introducing latency, connectivity dependence, and privacy concerns. While clinical studies demonstrate promising detection performance, real-world deployment remains limited by the lack of fully continuous, autonomous operation. Edge artificial intelligence (AI), which enables on-device deep-learning inference directly on smartwatches, represents a key advancement toward real-time, scalable AFib detection. By eliminating reliance on cloud infrastructure, edge AI reduces latency, enhances privacy, and supports immediate alerts during transient arrhythmic events. However, practical implementation requires careful optimization of model efficiency, power consumption, and hardware constraints alongside clinical validation. Future progress will depend on multi-objective design strategies that integrate accuracy, latency, and energy efficiency, as well as collaboration among engineers, clinicians, and regulators. Addressing challenges such as alert fatigue, equitable access, and data governance will be essential. Ultimately, edge AI has the potential to transform AFib management from reactive diagnosis to continuous, preventive monitoring, functioning as an unobtrusive, always-available cardiac safeguard.

Atrial fibrillation affects over 30 million people worldwide and requires long-term anticoagulation, with warfarin still widely used due to its efficacy and reversibility, but its narrow therapeutic window (INR 2.0–3.0) makes dosing particularly challenging, especially in high bleeding-risk patients where both under- and over-anticoagulation can lead to serious complications. Conventional dosing approaches rely on population-based nomograms and clinician judgment, failing to capture individual variability driven by genetics, diet, comorbidities, and drug interactions. To address this limitation, this article proposes a conceptual framework that integrates deep reinforcement learning with a safety-shield mechanism for personalized warfarin dosing. The system uses a deep Q-network trained on historical patient trajectories within an offline Markov Decision Process to recommend dose adjustments based on INR history and clinical risk factors, while a deterministic rule-based safety layer blocks unsafe actions, such as dose increases when INR exceeds 3.5 or extreme adjustments requiring clinician review. Conservative offline reinforcement learning further reduces the risk of unsafe policy extrapolation by limiting overestimation of out-of-distribution actions. Together, this hybrid architecture aims to improve time in therapeutic range while minimizing bleeding risk, providing a structured and clinically constrained approach for safer, individualized anticoagulation management in high-risk atrial fibrillation patients.