Artificial Intelligence in Detecting Heart Failure - A Systematic Review

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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Published
2025-05-01
DOI
https://doi.org/10.31986/issn.2689-0690_rdw.stratford_research_day.153_2025
Primary Topic
Artificial Intelligence in Healthcare
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article
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article

Artificial Intelligence in Detecting Heart Failure - A Systematic Review

Karol Kowalski, A. Sen, Dale Shober
Artificial Intelligence in Healthcare
article

Artificial Intelligence in Detecting Heart Failure - A Systematic Review

Karol Kowalski, A. Sen, Dale Shober
article en

Abstract

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.

Rowan University (US)
Openalex Percentile: Top 74%
Artificial Intelligence in Healthcare
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