Abstract Background Machine Learning (ML) is a type of algorithm that autonomously learns to recognize complex patterns. In the diagnostic context of cardiac arrhythmias, these algorithms have shown significant advancements due to their ability to provide automated interpretation and pattern recognition in electrocardiograms (ECGs). Objective To analyze and identify the applicability, validity, and feasibility of ML algorithm models in the diagnostic process of cardiac arrhythmias through automated electrocardiogram interpretation. Methods This systematic literature review was reported according to the PRISMA […]

AI talks about the role of AI as a reviewer of medical papers: The peer review process is a cornerstone of scientific integrity, particularly in the field of medicine, where rigorous evaluation ensures that published research is credible, reproducible, and safe for clinical application. However, the growing volume of medical articles submitted for publication, coupled with the complexity of modern research, has strained the traditional peer review system. The use of AI in the peer review of medical articles represents […]

The use of decision support tools and automation in medical practice is not new. Early tools employed simple regression models to reduce heterogeneity and offer more structured care. In cardiology, initial examples of decision support tools included the Framingham risk scores for primary prevention risk stratification and automated sliding scale dose adjustments for heparin or warfarin. Since then, automation processes have become more sophisticated and are now widespread in many routine aspects of medical practice. In cardiology, these include automated […]

Abstract Background Exercise training programs improve exercise capacity and quality of life (QoL) in patients with coronary artery disease (CAD). Although artificial intelligence (AI) has been used to design such programs, there are still few studies evaluating their effectiveness. Objectives This study compared the effects of technology-based and traditional programs for cardiac rehabilitation (CR) on exercise capacity and participation in patients with CAD using AI for data analysis. Methods A total of 52 patients with CAD were randomly assigned to […]

Abstract Background Heart failure (HF) is a disease associated with an important type of morbidity and mortality. The electrocardiogram (ECG), one of the tests used to evaluate HF, is low-cost and widely available. Objective To evaluate the performance of an artificial intelligence (AI) algorithm applied to ECG to detect HF and compare it with the predictive power of major electrocardiographic alterations (MEA). Methods This work is a diagnostic accuracy cross-sectional study. All participants were from the Longitudinal Study of Adult […]

Abstract Background: Multivariate prognostic analysis has been traditionally performed by regression models. However, many algorithms capable of translating an infinity of patterns into probabilities have emerged. The comparative accuracy of artificial intelligence and traditional statistical models has not been established in the medical field. Objective: To test the artificial intelligence as an accurate algorithm for predicting coronary disease in the scenario of acute chest pain and evaluate whether its performance is superior to traditional statistical model. Methods: A consecutive sample […]