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SYSTEMATIC REVIEW article

Front. Cardiovasc. Med.
Sec. Clinical and Translational Cardiovascular Medicine
Volume 12 - 2025 | doi: 10.3389/fcvm.2025.1521464
This article is part of the Research Topic Novel Translational Advances in Artificial Intelligence for Diagnosis and Treatment of Cardiovascular Diseases View all 8 articles

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

Provisionally accepted
Shanshan Chen Shanshan Chen 1,2Changde Wu Changde Wu 1Zhaojie Zhang Zhaojie Zhang 3Lingjuan Liu Lingjuan Liu 1Yike Zhu Yike Zhu 1Dingji Hu Dingji Hu 1Chenhui Jin Chenhui Jin 1Haoya Fu Haoya Fu 1Jing Wu Jing Wu 1Songqiao Liu Songqiao Liu 1*
  • 1 Southeast University, Nanjing, China
  • 2 Second Affiliated Hospital, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
  • 3 Lishui City People's Hospital, Lishui, China

The final, formatted version of the article will be published soon.

    Purpose: To 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.Methods: A 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.Results: A 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.Conclusions: AI 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.

    Keywords: artificial intelligence, machine learning, Aortic Valve Stenosis, Bibliometrics, Clinical decision support

    Received: 01 Nov 2024; Accepted: 27 Jan 2025.

    Copyright: © 2025 Chen, Wu, Zhang, Liu, Zhu, Hu, Jin, Fu, Wu and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Songqiao Liu, Southeast University, Nanjing, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.