Artificial intelligence-assisted early warning and continuousmonitoring system for prevention of cardiac emergencies: Acomprehensive review

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide, accounting for millions of deaths annually. A significant proportion of these deaths result from preventable cardiac emergencies, including acute myocardial infarction, cardiac arrest, malignant arrhythmias, and acute heart failure, where delayed recognition and intervention substantially worsen clinical outcomes. Conventional monitoring approaches rely primarily on intermittent clinical assessments and manual interpretation of physiological data, limiting their ability to detect subtle physiological deterioration before the onset of critical events. Recent advances in artificial intelligence (AI), machine learning (ML), deep learning (DL), wearable biosensors, cloud computing, and the Internet of Medical Things (IoMT) have transformed cardiovascular monitoring by enabling continuous, real-time analysis of multidimensional physiological signals. AI-assisted early warning systems integrate electrocardiographic signals, heart rate variability, blood pressure, oxygen saturation, respiratory rate, activity patterns, and patient history to predict impending cardiac emergencies before clinical manifestations become severe. Predictive algorithms facilitate early diagnosis, risk stratification, personalized intervention, and timely clinical decision-making, thereby improving patient outcomes while reducing healthcare costs and hospital readmissions. Moreover, wearable technologies and remote patient monitoring platforms have expanded cardiac surveillance beyond hospital settings, allowing continuous monitoring of high-risk individuals in their homes and communities. Despite these promising developments, challenges related to data privacy, algorithm transparency, interoperability, regulatory approval, model bias, cybersecurity, and ethical considerations continue to hinder widespread clinical implementation. This review comprehensively summarizes the principles, technological advancements, clinical applications, benefits, limitations, and future prospects of AI-assisted early warning and continuous monitoring systems for the prevention of cardiac emergencies. The review further discusses emerging innovations such as explainable AI, federated learning, digital twins, edge computing, and multimodal predictive analytics, which are expected to revolutionize cardiovascular care through precision medicine and proactive disease prevention. Overall, AI-assisted cardiac monitoring represents a paradigm shift from reactive healthcare toward predictive, preventive, and personalized cardiovascular management