A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram
Abstract Background Artificial intelligence (AI) is a modern tool that increases the diagnostic precision of the classical electrocardiogram (ECG). The objective of this bibliometric analysis was to identify the 50 most cited articles in the domain of AI in ECG, emphasizing publication trends, citat...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-05-01
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| Series: | The Egyptian Heart Journal |
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| Online Access: | https://doi.org/10.1186/s43044-025-00647-x |
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| author | Muhammad Arslan Ul Hassan Sana Mushtaq Abdul Rehman Mohammed Abdulkarem Al-Qaisi Zhen Yang |
| author_facet | Muhammad Arslan Ul Hassan Sana Mushtaq Abdul Rehman Mohammed Abdulkarem Al-Qaisi Zhen Yang |
| author_sort | Muhammad Arslan Ul Hassan |
| collection | DOAJ |
| description | Abstract Background Artificial intelligence (AI) is a modern tool that increases the diagnostic precision of the classical electrocardiogram (ECG). The objective of this bibliometric analysis was to identify the 50 most cited articles in the domain of AI in ECG, emphasizing publication trends, citation metrics, prominent authors and journals, leading institutions, and significant contributing countries. Results The 50 most cited articles on AI in ECG were published between 2000 and 2020 across 25 journals. The mean citations per article were 488.0, with the highest citations count being 1870. ‘IEEE Transactions on Biomedical Engineering’ and ‘Computers in Biology and Medicine’ published the highest number of articles, while Rajendra Acharya U and RS Tan were the most contributing authors. The USA and China had a total of 14 publications, and Singapore was the country with most collaborations. Conclusions This bibliometric analysis provides clinicians and researchers with an overview of evolution and progression of AI in the domain of ECG. Improved collaborations among different countries and institutions are essential for achieving advancements in the utilization of AI in ECG. |
| format | Article |
| id | doaj-art-c9b2ccf92b064fbd8e9ac117cf283643 |
| institution | DOAJ |
| issn | 2090-911X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | The Egyptian Heart Journal |
| spelling | doaj-art-c9b2ccf92b064fbd8e9ac117cf2836432025-08-20T03:22:03ZengSpringerOpenThe Egyptian Heart Journal2090-911X2025-05-0177111210.1186/s43044-025-00647-xA bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogramMuhammad Arslan Ul Hassan0Sana Mushtaq1Abdul Rehman2Mohammed Abdulkarem Al-Qaisi3Zhen Yang4Ningxia Medical UniversityNingxia Medical UniversityNingxia Medical UniversityNingxia Medical UniversityGeneral Hospital of Ningxia Medical UniversityAbstract Background Artificial intelligence (AI) is a modern tool that increases the diagnostic precision of the classical electrocardiogram (ECG). The objective of this bibliometric analysis was to identify the 50 most cited articles in the domain of AI in ECG, emphasizing publication trends, citation metrics, prominent authors and journals, leading institutions, and significant contributing countries. Results The 50 most cited articles on AI in ECG were published between 2000 and 2020 across 25 journals. The mean citations per article were 488.0, with the highest citations count being 1870. ‘IEEE Transactions on Biomedical Engineering’ and ‘Computers in Biology and Medicine’ published the highest number of articles, while Rajendra Acharya U and RS Tan were the most contributing authors. The USA and China had a total of 14 publications, and Singapore was the country with most collaborations. Conclusions This bibliometric analysis provides clinicians and researchers with an overview of evolution and progression of AI in the domain of ECG. Improved collaborations among different countries and institutions are essential for achieving advancements in the utilization of AI in ECG.https://doi.org/10.1186/s43044-025-00647-xArtificial intelligenceElectrocardiogramECGBibliometric analysis |
| spellingShingle | Muhammad Arslan Ul Hassan Sana Mushtaq Abdul Rehman Mohammed Abdulkarem Al-Qaisi Zhen Yang A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram The Egyptian Heart Journal Artificial intelligence Electrocardiogram ECG Bibliometric analysis |
| title | A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram |
| title_full | A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram |
| title_fullStr | A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram |
| title_full_unstemmed | A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram |
| title_short | A bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram |
| title_sort | bibliometric analysis of the 50 most cited articles about artificial intelligence in electrocardiogram |
| topic | Artificial intelligence Electrocardiogram ECG Bibliometric analysis |
| url | https://doi.org/10.1186/s43044-025-00647-x |
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