The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis

The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis

PurposeTo explore the expanding role of artificial intelligence (AI) in managing aortic valve stenosis (AVS) by bibliometric analysis to identify research trends, key contributors, and the impact of AI on enhancing diagnostic and therapeutic strategies for AVS.MethodsA comprehensive literature revie...

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Bibliographic Details
Main Authors: Shanshan Chen, Changde Wu, Zhaojie Zhang, Lingjuan Liu, Yike Zhu, Dingji Hu, Chenhui Jin, Haoya Fu, Jing Wu, Songqiao Liu
Format: Article
Language:English
Published: Frontiers Media S.A. 2025-02-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
artificial intelligence
machine learning
aortic valve stenosis
bibliometrics
clinical decision support
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2025.1521464/full
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Summary:PurposeTo explore the expanding role of artificial intelligence (AI) in managing aortic valve stenosis (AVS) by bibliometric analysis to identify research trends, key contributors, and the impact of AI on enhancing diagnostic and therapeutic strategies for AVS.MethodsA comprehensive literature review was conducted using the Web of Science database, covering publications from January 1990 to March 2024. Articles were analyzed with bibliometric tools such as CiteSpace and VOSviewer to identify key research trends, core authors, institutions, and research hotspots in AI applications for AVS.ResultsA total of 118 articles were analyzed, showing a significant increase in publications from 2014 onwards. The results highlight the growing impact of AI in AVS, particularly in cardiac imaging and predictive modeling. Core authors and institutions, primarily from the U.S. and Germany, are driving research in this field. Key research hotspots include machine learning applications in diagnostics and personalized treatment strategies.ConclusionsAI is playing a transformative role in the diagnosis and treatment of AVS, improving accuracy and personalizing therapeutic approaches. Despite the progress, challenges such as model transparency and data security remain. Future research should focus on overcoming these challenges while enhancing collaboration among international institutions to further advance AI applications in cardiovascular medicine.
ISSN:2297-055X

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