Integration of artificial intelligence into multidisciplinary diagnostics of cardiovascular and neurological diseases

Abstract

Cardiovascular and neurological diseases are leading causes of morbidity and mortality worldwide, and their close pathophysiological relationship, known as the brain-heart axis, requires a comprehensive multidisciplinary approach to diagnosis. This review analyzes and summarizes research conducted primarily over the past five years on the integration of artificial intelligence technologies into the diagnostics of this group of diseases. The review reveals that artificial intelligence, particularly deep learning models, demonstrates transformative potential in the analysis of electrocardiograms, neuroimaging, and multimodal clinical data, providing significant improvements in accuracy and early detection of pathologies. Key advances include the ability of artificial intelligence algorithms to identify hidden disease markers inaccessible to human perception and to predict the risk of conditions such as atrial fibrillation and ischemic stroke. However, the widespread clinical implementation of artificial intelligence faces significant challenges, including black box, systemic bias in training data, poor model generalization, and a severe lack of evidence from large-scale prospective clinical trials. The paper concludes that realizing the potential of artificial intelligence for personalized predictive medicine in neurocardiology is only possible by overcoming existing technical, ethical, and regulatory barriers through interdisciplinary collaboration.

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