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This article discusses the increasing use of machine learning (ML) algorithms in the medical field, specifically in the subfield of heart failure (HF). While these algorithms offer many advantages, there are also challenges such as data collection and preprocessing, overtraining, and explicability. The article highlights the potential uses of ML in HF, including discovering new knowledge, predicting outcomes, and assisting in diagnosis and decision-making. The goal of the article is to update readers on the expanding applications and limitations of ML in HF.