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<p>Cardiovascular medicine is characterized by high data complexity: imaging, high-resolution electrocardiograms (ECG), wearables and electronic patient records generate large heterogeneous datasets that can only be partially analyzed using conventional methods. Artificial intelligence (AI) serves as a&#xa0;complementary tool to identify patterns, make predictions and supporting for clinical decision-making. This article provides a&#xa0;practical overview of AI applications, beginning with a&#xa0;methodological framework in relation to classical statistics, followed by detailed examples from imaging and ECG analysis. Deep learning models enable automated image segmentation, calculation of left ventricular ejection fraction and prediction of structural heart disease. Further applications include heart failure, interventional cardiology and risk stratification. Generative AI provides new possibilities in documentation, patient education and training but requires consideration of limitations, such as hallucinations, data privacy and limited of validation. Finally, implementation, regulatory frameworks and ethical aspects are discussed. The use of AI improves reproducibility, efficiency and diagnostic information; however, it does not replace clinical expertise but transforms the role of clinicians in practice.</p>

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  • 期刊论文

  • 发表日期：

    2026年2月16日

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  • Axel Loewe

摘要

Cardiovascular medicine is characterized by high data complexity: imaging, high-resolution electrocardiograms (ECG), wearables and electronic patient records generate large heterogeneous datasets that can only be partially analyzed using conventional methods. Artificial intelligence (AI) serves as a complementary tool to identify patterns, make predictions and supporting for clinical decision-making. This article provides a practical overview of AI applications, beginning with a methodological framework in relation to classical statistics, followed by detailed examples from imaging and ECG analysis. Deep learning models enable automated image segmentation, calculation of left ventricular ejection fraction and prediction of structural heart disease. Further applications include heart failure, interventional cardiology and risk stratification. Generative AI provides new possibilities in documentation, patient education and training but requires consideration of limitations, such as hallucinations, data privacy and limited of validation. Finally, implementation, regulatory frameworks and ethical aspects are discussed. The use of AI improves reproducibility, efficiency and diagnostic information; however, it does not replace clinical expertise but transforms the role of clinicians in practice.