Global research trends in the application of artificial intelligence in oncology care: a bibliometric study
ObjectiveTo use bibliometric methods to analyze the prospects and development trends of artificial intelligence(AI) in oncology nursing from 1994 to 2024, providing guidance and reference for oncology nursing professionals and researchers.MethodsThe core set of the Web of Science database was search...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2024.1456144/full |
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| author | Mianmian Xu Yafang Chen Tianen Wu Yuyan Chen Wanling Zhuang Yinhui Huang Chuanzhen Chen |
| author_facet | Mianmian Xu Yafang Chen Tianen Wu Yuyan Chen Wanling Zhuang Yinhui Huang Chuanzhen Chen |
| author_sort | Mianmian Xu |
| collection | DOAJ |
| description | ObjectiveTo use bibliometric methods to analyze the prospects and development trends of artificial intelligence(AI) in oncology nursing from 1994 to 2024, providing guidance and reference for oncology nursing professionals and researchers.MethodsThe core set of the Web of Science database was searched for articles from 1994 to 2024. The R package “Bibliometrix” was used to analyze the main bibliometric features, creating a three-domain chart to display relationships among institutions, countries, and keywords. VOSviewer facilitated co-authorship analysis and its visualization was used for co- occurrence analysis. CiteSpace calculated citation bursts and keyword occurrences.ResultsA total of 517 articles were retrieved, representing 80 countries/regions. The United States had the highest number of publications, with 188 articles (36.4%), followed by China with 79 articles (15.3%). The top 10 institutions in terms of publication output were all U.S.-based universities or cancer research institutes, with Harvard University ranking first. Prominent research teams, such as those led by Repici, Aerts, and Almangush, have made significant contributions to studies on AI in tumor risk factor identification and symptom management. In recent years, the keywords with the highest burst strength were “model” and “human papillomavirus.” The most studied tumor type was breast cancer. While Cancers published the highest number of articles, journals such as CA: A Cancer Journal for Clinicians and PLOS ONE had higher impact and citation rates.ConclusionBy analyzing the volume of AI literature in oncology nursing, combined with the statistical analysis of institutions, core authors, journals, and keywords, the research hotspots and trends in the application of AI in oncology nursing over the past 30 years are revealed. AI in oncology nursing is entering a stage of rapid development, providing valuable reference for scholars and professionals in the field. |
| format | Article |
| id | doaj-art-66e926a0100f4dbfb79bb95f71ff70d0 |
| institution | Kabale University |
| issn | 2234-943X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj-art-66e926a0100f4dbfb79bb95f71ff70d02025-01-07T05:23:59ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2025-01-011410.3389/fonc.2024.14561441456144Global research trends in the application of artificial intelligence in oncology care: a bibliometric studyMianmian Xu0Yafang Chen1Tianen Wu2Yuyan Chen3Wanling Zhuang4Yinhui Huang5Chuanzhen Chen6Department of Urinary Surgery, Jinjiang Municipal Hospital, Quanzhou, ChinaDepartment of Neurology, Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, ChinaDepartment of Urinary Surgery, Jinjiang Municipal Hospital, Quanzhou, ChinaDepartment of Urinary Surgery, Jinjiang Municipal Hospital, Quanzhou, ChinaDepartment of Urinary Surgery, Jinjiang Municipal Hospital, Quanzhou, ChinaDepartment of Neurology, Jinjiang Municipal Hospital, Quanzhou, ChinaDepartment of Nursing, Jinjiang Municipal Hospital, Quanzhou, ChinaObjectiveTo use bibliometric methods to analyze the prospects and development trends of artificial intelligence(AI) in oncology nursing from 1994 to 2024, providing guidance and reference for oncology nursing professionals and researchers.MethodsThe core set of the Web of Science database was searched for articles from 1994 to 2024. The R package “Bibliometrix” was used to analyze the main bibliometric features, creating a three-domain chart to display relationships among institutions, countries, and keywords. VOSviewer facilitated co-authorship analysis and its visualization was used for co- occurrence analysis. CiteSpace calculated citation bursts and keyword occurrences.ResultsA total of 517 articles were retrieved, representing 80 countries/regions. The United States had the highest number of publications, with 188 articles (36.4%), followed by China with 79 articles (15.3%). The top 10 institutions in terms of publication output were all U.S.-based universities or cancer research institutes, with Harvard University ranking first. Prominent research teams, such as those led by Repici, Aerts, and Almangush, have made significant contributions to studies on AI in tumor risk factor identification and symptom management. In recent years, the keywords with the highest burst strength were “model” and “human papillomavirus.” The most studied tumor type was breast cancer. While Cancers published the highest number of articles, journals such as CA: A Cancer Journal for Clinicians and PLOS ONE had higher impact and citation rates.ConclusionBy analyzing the volume of AI literature in oncology nursing, combined with the statistical analysis of institutions, core authors, journals, and keywords, the research hotspots and trends in the application of AI in oncology nursing over the past 30 years are revealed. AI in oncology nursing is entering a stage of rapid development, providing valuable reference for scholars and professionals in the field.https://www.frontiersin.org/articles/10.3389/fonc.2024.1456144/fullartificial intelligenceoncology nursingcancer preventionbibliometricsVOSviewerCiteSpace |
| spellingShingle | Mianmian Xu Yafang Chen Tianen Wu Yuyan Chen Wanling Zhuang Yinhui Huang Chuanzhen Chen Global research trends in the application of artificial intelligence in oncology care: a bibliometric study Frontiers in Oncology artificial intelligence oncology nursing cancer prevention bibliometrics VOSviewer CiteSpace |
| title | Global research trends in the application of artificial intelligence in oncology care: a bibliometric study |
| title_full | Global research trends in the application of artificial intelligence in oncology care: a bibliometric study |
| title_fullStr | Global research trends in the application of artificial intelligence in oncology care: a bibliometric study |
| title_full_unstemmed | Global research trends in the application of artificial intelligence in oncology care: a bibliometric study |
| title_short | Global research trends in the application of artificial intelligence in oncology care: a bibliometric study |
| title_sort | global research trends in the application of artificial intelligence in oncology care a bibliometric study |
| topic | artificial intelligence oncology nursing cancer prevention bibliometrics VOSviewer CiteSpace |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2024.1456144/full |
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