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Updated: Sep 17, 2025

Lumped-Parameter and Finite Element Modeling of Heart Failure with Preserved Ejection Fraction
Published on: February 13, 2021
The emerging role of artificial intelligence in heart failure
Brett S Bernstein1,2, Sona Streather3, Kevin O'Gallagher1,2
1School of Cardiovascular and Metabolic Medicine & Sciences, British Heart Foundation Centre of Research Excellence, King's College London, London, UK.
None:
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.
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