Application of Artificial Intelligence in Cardiovascular Disease Diagnostics: Opportunities and Challenges

Backround: Cardiovascular diseases (CVDs) remain the leading cause of mortality worldwide and represent a major burden on healthcare systems. In recent years (2015–2025), rapid advances in artificial intelligence (AI), particularly in deep learning and multimodal data integration, have significantly improved cardiovascular diagnostics. Aims: The aim of this study is to analyze current applications of AI in cardiology and to evaluate their impact on healthcare systems. Methods: This narrative review is based on recent scientific literature and focuses on electrocardiogram (ECG) analysis, cardiovascular imaging, and risk prediction models. Results: The findings indicate that AI improves diagnostic accuracy, reduces time to diagnosis, enhances clinical workflows, and supports healthcare efficiency. Furthermore, AI contributes to improved accessibility and better allocation of medical resources. However, challenges such as data bias, limited interpretability, ethical concerns, and regulatory barriers remain significant. Conclusion: AI has strong potential to transform healthcare systems but should be considered a complementary tool supporting clinical decision-making rather than replacing healthcare professionals.