Use of Artificial Intelligence in the Prediction of Cardiac Arrhythmia: From Algorithm to Heartbeat.

Abstract

Cardiac arrhythmias represent a significant public health challenge, often leading to severe complications such as stroke and sudden cardiac death. This narrative review explores the role of artificial intelligence in predicting these conditions, highlighting its potential to enhance diagnostic accuracy and patient outcomes. The introduction discusses the limitations of traditional methods and the emergence of artificial intelligence driven approaches. Objectives include synthesizing evidence on artificial intelligence applications in arrhythmia prediction, evaluating their efficacy, and identifying future directions. Methodology involved a comprehensive search across databases including PubMed, Scopus, and Web of Science, using keywords such as "artificial intelligence," "machine learning," "cardiac arrhythmias," and "prediction models." Articles published within the last 10 years were prioritized, excluding those outside the scope. Results from selected studies demonstrate artificial intelligence models, particularly those using deep learning and electrocardiogram analysis, achieving high accuracy rates (up to 99,35%) in detecting arrhythmias like atrial fibrillation. Discussion addresses challenges such as data privacy and model interpretability, while emphasizing clinical implications. Conclusions underscore artificial intelligence transformative potential in cardiology, advocating for integrated clinical adoption and further research.