Abstract
Early detection of cardiovascular diseases (CVDs) is crucial for reducing morbidity and mortality. Artificial intelligence (AI)–assisted diagnostic tools—spanning deep-learning analysis of electrocardiograms (ECGs), AI interpretation of coronary CT angiography (CCTA), machine-learning risk stratification, and wearable-based predictive monitoring—have rapidly advanced and begun entering clinical workflows. This article critically evaluates the effectiveness of current AI tools for early CVD detection, comparing diagnostic accuracy, clinical utility, regulatory progress, and real-world performance. We synthesize evidence from algorithm validation studies, clinical trials, and real-world deployments, discuss limitations (bias, data quality, integration barriers), and outline practical recommendations for safe, equitable implementation in routine care.
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Copyright (c) 2024 Saima Parveen, Omar Saeed (Author)