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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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1521464/full |
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| author | Shanshan Chen Shanshan Chen Changde Wu Zhaojie Zhang Lingjuan Liu Yike Zhu Dingji Hu Chenhui Jin Haoya Fu Jing Wu Songqiao Liu Songqiao Liu Songqiao Liu |
| author_facet | Shanshan Chen Shanshan Chen Changde Wu Zhaojie Zhang Lingjuan Liu Yike Zhu Dingji Hu Chenhui Jin Haoya Fu Jing Wu Songqiao Liu Songqiao Liu Songqiao Liu |
| author_sort | Shanshan Chen |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-57b51eaa72124eeb80ff92c20126abbe |
| institution | Kabale University |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-57b51eaa72124eeb80ff92c20126abbe2025-02-12T07:25:56ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-02-011210.3389/fcvm.2025.15214641521464The role of artificial intelligence in aortic valve stenosis: a bibliometric analysisShanshan Chen0Shanshan Chen1Changde Wu2Zhaojie Zhang3Lingjuan Liu4Yike Zhu5Dingji Hu6Chenhui Jin7Haoya Fu8Jing Wu9Songqiao Liu10Songqiao Liu11Songqiao Liu12Department of Respiratory and Critical Care Medicine, Second Affiliated Hospital of Xuzhou Medical University, Xuzhou Mining Group General Hospital, Xuzhou, Jiangsu, ChinaJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, ChinaJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, ChinaDepartment of Critical Care Medicine, Trauma Center, Nanjing Lishui People’s Hospital, Zhongda Hospital Lishui Branch, Nanjing, Jiangsu, ChinaJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, ChinaJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, ChinaJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, ChinaJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, ChinaJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, ChinaJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, ChinaJiangsu Provincial Key Laboratory of Critical Care Medicine, Department of Critical Care Medicine, Trauma Center, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu, ChinaDepartment of Critical Care Medicine, Trauma Center, Nanjing Lishui People’s Hospital, Zhongda Hospital Lishui Branch, Nanjing, Jiangsu, ChinaThe First People’s Hospital of Lianyungang, The Lianyungang Clinical College of Nanjing Medical University, The First Affiliated Hospital of Kangda College of Nanjing Medical University, The Affiliated Lianyungang Hospital of Xuzhou Medical University, Lianyungang, Jiangsu, ChinaPurposeTo 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.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1521464/fullartificial intelligencemachine learningaortic valve stenosisbibliometricsclinical decision support |
| spellingShingle | Shanshan Chen Shanshan Chen Changde Wu Zhaojie Zhang Lingjuan Liu Yike Zhu Dingji Hu Chenhui Jin Haoya Fu Jing Wu Songqiao Liu Songqiao Liu Songqiao Liu The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis Frontiers in Cardiovascular Medicine artificial intelligence machine learning aortic valve stenosis bibliometrics clinical decision support |
| title | The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis |
| title_full | The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis |
| title_fullStr | The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis |
| title_full_unstemmed | The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis |
| title_short | The role of artificial intelligence in aortic valve stenosis: a bibliometric analysis |
| title_sort | role of artificial intelligence in aortic valve stenosis a bibliometric analysis |
| topic | artificial intelligence machine learning aortic valve stenosis bibliometrics clinical decision support |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1521464/full |
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