Brett S Bernstein, Sona Streather, Kevin O'Gallagher
{"title":"The emerging role of artificial intelligence in heart failure.","authors":"Brett S Bernstein, Sona Streather, Kevin O'Gallagher","doi":"10.1080/14796678.2025.2523155","DOIUrl":null,"url":null,"abstract":"<p><p>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.</p>","PeriodicalId":12589,"journal":{"name":"Future cardiology","volume":" ","pages":"1-7"},"PeriodicalIF":1.6000,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Future cardiology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/14796678.2025.2523155","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0
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.
期刊介绍:
Research advances have contributed to improved outcomes across all specialties, but the rate of advancement in cardiology has been exceptional. Concurrently, the population of patients with cardiac conditions continues to grow and greater public awareness has increased patients" expectations of new drugs and devices. Future Cardiology (ISSN 1479-6678) reflects this new era of cardiology and highlights the new molecular approach to advancing cardiovascular therapy. Coverage will also reflect the major technological advances in bioengineering in cardiology in terms of advanced and robust devices, miniaturization, imaging, system modeling and information management issues.