Machine learning applications in cardiology

Cardiovascular diseases remain the leading cause of death globally and impose significant economic burdens. The growing prevalence of cardiovascular diseases underscores the need for advanced prevention and management strategies. Artificial intelligence, specifically with machine learning and deep learning, offers transformative potential in cardiology for a wide range of tasks. This thesis explores the application of artificial intelligence in cardiovascular care, focusing on clinical prediction models, integration of multimodal data, and the development of algorithms for specific cardiovascular conditions. Additionally, it addresses the challenges of model validation and real-world applicability, proposing rigorous methodologies for improving artificial intelligence’s role in cardiology care.