Systematic review assesses efficacy of AI-ECG models in detecting heart failure, indicating gaps in research and implementation barriers.
Many studies have presented an artificial intelligence (AI) model to read electrocardiograms (ECGs) for the purpose of detecting or classifying the severity of heart failure. While most literature suggests that these AI-ECG models are effective at detecting or classifying heart failure, their efficacy in comparison to the current standards of care is not well researched. Furthermore, the barriers to implementation and the risks of these systems have not been studied and compiled. This literature review aims to outline the efficacy of AI-ECG models, determine how effective AI-ECG is in comparison to the standards of care, and outline the barriers to clinical implementation.
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Shober et al. (2025) studied this question.
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