AUTHOR=Liu Yangli , Zhao Lei , Tu Bin , Wang Jie , He Yaqun , Jiang Rufang , Wu Xiaofeng , Wen Wen , Liu Jian 

TITLE=Application of artificial intelligence in echocardiography from 2009 to 2024: a bibliometric analysis

JOURNAL=Frontiers in Medicine

VOLUME=Volume 12 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2025.1587364

DOI=10.3389/fmed.2025.1587364

ISSN=2296-858X

ABSTRACT=BackgroundEchocardiography is a cornerstone in the clinical diagnosis of cardiovascular diseases, providing critical insights into cardiac structure and function. Over recent years, artificial intelligence (AI) has emerged as a transformative adjunct to traditional echocardiographic techniques, enhancing diagnostic accuracy through innovations such as automatic view labeling, advanced image segmentation, and predictive disease modeling. The objective of this study is to explore the current status and prevailing research trends in this field from 2009 to 2024 through bibliometric analysis and to forecast future developmental trajectories.MethodsWe selected the Science Citation Index Expanded (SCI-Expanded) from the Web of Science Core Collection (WOSCC) as our primary data source and conducted a comprehensive search encompassing all articles and reviews published between 2009 and 2024 and used the online analysis platform of bibliometrics, CiteSpace and VOSviewer software to analyze countries/regions, institutions, authors, keywords, and references, used Microsoft Excel 2021 to visualize the trends of the number of articles published by year.ResultsBetween 2009 and 2024, a total of 3,411 publications on AI applications in echocardiography were identified, including 3,000 articles (87.9%) and 411 reviews (12.1%), contributed by researchers from 100 countries/regions. China and the USA were the leading contributors in terms of publication volume. Notably, institutions such as Shanghai Jiaotong University demonstrated strong research productivity and international collaboration. Journal of the American College of Cardiology ranked among the most influential journals in this domain. Keyword analysis revealed that terms such as “artificial intelligence,” “machine learning,” “deep learning,” and “echocardiography” are central research hotspots, indicating emerging trends in the field and the potential to evolve into major areas of future investigation.ConclusionOver the past decade, the integration of AI with echocardiography has become increasingly sophisticated. This study highlights the critical contributions of AI applications in echocardiography to the progression of the field and offers valuable insights for researchers embarking on future investigations.