Keywords
Heart Failure
Early diagnosis
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Abstract
Objective: To understand the role of artificial intelligence (AI) in the detection, diagnosis, monitoring, and prognosis of heart failure (HF). Methods: This is an integrative literature review, in which the PubMed Central (PMC) database was used. Initially, 945 articles were identified and subjected to eligibility criteria. Among these, 15 articles were selected for the development of this study. Results: HF is one of the main chronic cardiovascular conditions, whose current clinical management increasingly requires the adoption of new technologies. In this context, artificial intelligence, through the application of algorithms such as machine learning (ML), convolutional neural networks (CNN), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN), plays an extremely relevant role in optimizing and early detection of this cardiac pathology. In addition to speed, these algorithms can identify patterns that may not be evident in traditional analyses, influencing the improvement of diagnostic and prognostic factors with the aim of enhancing clinical outcomes. Final considerations: Therefore, it is emphasized that despite the enormous potential of AI as a tool to assist in the clinical management of HF, further research is still needed to ensure the standardization of models, in order to ensure their effective implementation in clinical practice.
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