Artificial intelligence in cardiology — current applications, challenges, and future directions

Introduction: Rapid advances in artificial intelligence (AI), including machine learning (ML) and deep learning (DL), have significantly influenced cardiovascular medicine. Integration into cardiology is driven by the need for accurate, rapid, and reproducible diagnostics. Despite its promise, challenges persist in validation, reproducibility, data security, and ethical implementation. This review summarizes current AI applications across key cardiology domains: electrocardiography, cardiac imaging, risk prediction, heart failure, and interventional cardiology. It also identifies methodological, ethical, and regulatory challenges and outlines future directions for safe clinical adoption. Material and methods: We conducted a structured literature review of studies published between 2015 and 2025 using the PubMed/MEDLINE, Scopus, and Google Scholar databases. Research on AI in ECG analysis, echocardiography, computed tomography, coronary angiography, cardiac magnetic resonance, nuclear cardiology, risk prediction, and medical training was analyzed. Model performance, validation methods, clinical utility, and limitations were synthesized qualitatively. Results: AI shows high accuracy in arrhythmia detection, structural heart disease screening, automated image analysis, and outcome prediction. In imaging, DL enables faster, reproducible quantification across echocardiography, CT, CMR, and nuclear modalities. AI improves risk stratification for heart failure, myocardial infarction, and coronary artery disease, and supports decision-making and education via simulation and augmented reality. Limitations include reliance on large datasets, limited transparency, as well as data protection and regulatory concerns. Conclusions: AI can enhance diagnostic precision, workflow efficiency, and personalized cardiology care. Successful integration requires rigorous validation, explainable algorithms, secure data governance, and clear ethical and legal frameworks. Multicenter prospective studies and transparent AI processes remain essential for safe implementation.