Artificial Intelligence in Arrhythmia Detection: From Wearables to Implantables

Artificial Intelligence (AI) has transformed the detection and management of cardiac arrhythmias. These days, AI algorithms are included into a variety of monitoring systems, ranging from sophisticated implanted cardiac devices to consumergrade wearables. These devices enhance diagnostic accuracy, reduce healthcare costs, and offer continuous, non-invasive monitoring in both high-risk and general populations. In wearables such as fitness trackers and smartwatches, Artificial Intelligence (AI) integrates Photoplethysmography (PPG) and single-lead ECG data to detect arrhythmias, including Atrial Fibrillation (AF), with high sensitivity. Pacemakers and implanted loop recorders use Machine Learning (ML) to predict the incidence of arrhythmias, optimize therapy delivery, and provide real-time alerts. According to recent studies, deep learning models outperform traditional scoring techniques in predicting arrhythmia risk. Despite these developments, challenges remain related to regulatory approval, data privacy, model interpretability, and integration into clinical operations. This paper evaluates the data that is already available, looks at the latest advancements in AI-powered arrhythmia identification, and suggests. possible directions for further study in this rapidly evolving field. By bridging the gap between data science and clinical cardiology, the integration of AI with cardiac electrophysiology holds the potential to transform the management of arrhythmias.