MULTIDIMENSIONAL ATTENTION IN THE DIAGNOSIS OF STROKE: EVIDENCE OF THE USE OF ARTIFICIAL INTELLIGENCE IN HEALTHCARE PRACTICE

Abstract

Introduction: Artificial intelligence (AI) has emerged as an important resource in diagnostic medicine, especially in the medical fields of cardiology and neurology. Its application in the diagnosis of cardiovascular diseases and the management of stroke represents a major advance in clinical accuracy, speed, and efficiency. From this perspective, it becomes important to critically analyze the current contributions of Artificial Intelligence (AI) to medical practice. Objective: To investigate the efficiency of AI application in the diagnosis of stroke cases, as well as its benefits, limitations, and impacts in the clinical context. Methodology: This is a narrative review of articles published between 2021 and 2025, retrieved from the PubMed, SciELO, Virtual Health Library (BVS), and LILACS databases. Studies directly addressing the use of AI in echocardiography, electrocardiography, and neuroimaging associated with stroke were included. Results: Machine learning algorithms, such as convolutional neural networks, show high performance in the early detection of strokes and left ventricular systolic dysfunction. AI demonstrates high accuracy and predictive value, especially in ECG and echocardiogram exams, and enables fast and reliable analysis of brain images in the acute context. Conclusion: AI plays a promising and growing role in medicine, contributing to faster and more accurate diagnoses. However, its implementation requires rigorous criteria and professional training. The technology should be seen as a decision-support tool allied with clinical reasoning.