AI Benefits and Machine Learning Errors-Cardiovascular Disease, A systematic review
Published in Computational Sciences, General & Internal Medicine, and Mathematics
Methods: A systematic search of PubMed, Scopus, and IEEE Xplore (2018–2024) identified original studies comparing AI with standard clinical care or conventional algorithms. Results: Thirty-eight studies met the inclusion criteria. AI improved arrhythmia detection, cardiac imaging analysis, and heart failure risk prediction, often outperforming traditional approaches. However, machine learning errors were reported in 34% of studies, including false negatives, reduced performance across ethnic groups, and vulnerability to image perturbations. Conclusion: AI has substantial potential to enhance cardiovascular care by improving efficiency and diagnostic accuracy. Nevertheless, clinically significant errors remain common, particularly in underrepresented populations and complex cases. Safe implementation requires fairness assessment, uncertainty estimation, continuous monitoring, and transparent reporting of model performance across patient subgroups.
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BMC Cardiovascular Disorders
This journal is an open access, peer-reviewed journal that considers articles on the characterization, prevention, diagnosis and treatment of disorders of the heart and circulatory system.
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BMC Pulmonary Medicine
This journal is an open access, peer-reviewed journal that considers articles on all aspects of the prevention, diagnosis and management of pulmonary and associated disorders, as well as related molecular genetics, pathophysiology, and epidemiology.
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