Artificial Intelligence in Cardiovascular Disease Prediction and Management: Current Status and Future Directions

Abstract

Cardiovascular diseases (CVDs) remain the leading cause of morbidity and mortality worldwide, necessitating

innovative strategies for early diagnosis, risk stratification, and personalized management. The integration of artificial

intelligence (AI) into cardiovascular medicine has emerged as a transformative approach, offering enhanced diagnostic

accuracy, predictive analytics, and optimized therapeutic decision-making. Recent advancements in machine learning, deep

learning, and data-driven algorithms have demonstrated significant potential across various domains, including imaging

analysis, electrocardiography interpretation, and risk prediction models. Despite promising outcomes, several challenges, such

as data quality, model interpretability, ethical considerations, and regulatory hurdles continue to impede widespread clinical

adoption. This review aims to provide a comprehensive overview of the current applications of AI in cardiovascular disease

prediction and management, critically analyze existing limitations, and explore future directions to advance the integration of

AI technologies into routine cardiovascular care.