The emerging role of artificial intelligence in heart failure.

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Authors

Bernstein, Brett S
Streather, Sona
O'Gallagher, Kevin

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Issue Date

2025-07-03

Type

Journal Article
Review

Language

en

Keywords

Heart failure , biomarkers , echocardiography , imaging , pharmacogenetics

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Abstract

Heart Failure is a prevalent disease with significant impacts on morbidity and mortality. Heart failure patients have a large volume of healthcare data which is digitized and can be collated. Artificial intelligence (AI) can then be used to assess the data for underlying patterns. AI systems can be trained to analyze readily available data, such as ECGs and heart sounds, and assess likelihood of heart failure. AI can also risk stratify heart failure patients by analyzing available healthcare data. AI can allow rapid assignment of heart failure patients to specific groups via automated echo analysis, but can also provide information regarding novel imaging bio-markers that may be more useful than left ventricular ejection fraction, such as first phase ejection fraction. AI can be used to assess patients' suitability for existing drugs, whilst also enabling development of novel drugs for known or newly discovered drug targets. Heart Failure as a field, with its multi-modal data set and variability in outcomes, will greatly benefit from the expansion and improvement of AI technology over the next 20 years.
Heart failure is common. It leads to periods of being unwell and death. Details about these patients are kept by hospitals. Artificial Intelligence (AI) can analyze these, to work out how likely heart failure is. AI can also suggest how likely it is that these patients will become more unwell. AI can help with initial diagnosis, and tell you whether certain drugs will work. This is true for different types of heart failure, including “Heart Failure with Preserved Ejection fraction,” which is often overlooked as a diagnosis. We believe that AI will improve the quality and length of life in people with heart failure.

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Journal

Future cardiology

Volume

21

Issue

10

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ISSN

1744-8298

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