AI Benefits and Machine Learning Errors-Cardiovascular Disease

Abstract Background Cardiovascular disease (CVD) remains the leading cause of death worldwide. Artificial intelligence (AI) is increasingly used in cardiovascular care for diagnosis, risk prediction, imaging analysis, and clinical decision-making. This review evaluated both the benefits and limitations of AI applications in CVD. 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.