Artificial Intelligence in the Detection of Cardiovascular Diseases

Cardiovascular diseases remain the leading cause of mortality worldwide, posing an ongoing challenge for clinical practice and public health systems. Early detection of these conditions is essential to optimize patient prognosis and reduce the social and economic burden of their complications. Traditional methods such as electrocardiography (ECG) and echocardiography, though widely used, have significant limitations, particularly in the early stages of disease. For example, ECG interpretation can be highly subjective, which may lead to delayed diagnoses and suboptimal management.
In this context, Artificial Intelligence (AI) has emerged as a promising tool in cardiology, with significant advancements in the analysis of tests such as ECG, computed tomography (CT), and magnetic resonance imaging (MRI). Using machine learning and deep learning algorithms, AI has shown the ability to identify patterns that are imperceptible to the human eye and to anticipate subtle electrophysiological and structural changes associated with conditions such as atrial fibrillation and left ventricular systolic dysfunction (NAGARAJAN et al., 2021). Furthermore, the integration of data from wearable devices, electronic health records, and complementary tests enables continuous monitoring, supporting personalized medicine and proactive prevention.