A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023
Abstract Background Recent research has demonstrated that the use of artificial intelligence (AI) in radiotherapy (RT) has significantly streamlined the process for physicians to treat patients with tumors; however, bibliometric studies examining the correlation between AI and RT are not available....
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| Format: | Article |
| Language: | English |
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BMC
2024-11-01
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| Series: | Radiation Oncology |
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| Online Access: | https://doi.org/10.1186/s13014-024-02551-1 |
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| author | Minghe Lv Yue feng Su Zeng Yang Zhang Wenhao Shen Wenhui Guan Xiangyu E. Hongwei Zeng Ruping Zhao Jingping Yu |
| author_facet | Minghe Lv Yue feng Su Zeng Yang Zhang Wenhao Shen Wenhui Guan Xiangyu E. Hongwei Zeng Ruping Zhao Jingping Yu |
| author_sort | Minghe Lv |
| collection | DOAJ |
| description | Abstract Background Recent research has demonstrated that the use of artificial intelligence (AI) in radiotherapy (RT) has significantly streamlined the process for physicians to treat patients with tumors; however, bibliometric studies examining the correlation between AI and RT are not available. Providing a thorough overview of the knowledge structure and research hotspots between AI and RT was the main goal of the current study. Method A search was conducted on the Web of Science Core Collection (WoSCC) database for publications pertaining to AI and RT between 2003 and 2023. VOSviewers, CiteSpace, and the R program “bibliometrix” were used to do the bibliometric analysis. Results The analysis comprised 615 publications from 64 countries, with USA and China leading the pack. Since 2017, there have been more and more publications about RT and AI every year. The research center that made the biggest contribution to this topic was Maastricht University. The most articles published journal in this field was Frontiers in Oncology, while Medical Physics received the greatest number of citations. Dekker Andre is the author with the greatest number of published articles, while Philippe Lambin was the most often co-cited author. In the newly identified research hotspots, “autocontouring algorithm”, “deep learning”, and “machine learning” stand out as the main terms. Conclusion In fact, our bibliometric analysis offers insightful information on current research directions and advancements pertaining to the use of AI in RT. For academics looking to understand the connection between AI and RT, this study is a great resource because it highlights current research frontiers and hot trends. |
| format | Article |
| id | doaj-art-3d2dea9c5d564cad8c93d19dc17e2a7b |
| institution | Kabale University |
| issn | 1748-717X |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
| record_format | Article |
| series | Radiation Oncology |
| spelling | doaj-art-3d2dea9c5d564cad8c93d19dc17e2a7b2024-11-17T12:39:40ZengBMCRadiation Oncology1748-717X2024-11-0119111310.1186/s13014-024-02551-1A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023Minghe Lv0Yue feng1Su Zeng2Yang Zhang3Wenhao Shen4Wenhui Guan5Xiangyu E.6Hongwei Zeng7Ruping Zhao8Jingping Yu9Department of Radiotherapy, Shuguang Hospital, Shanghai University of Chinese Traditional MedicineDepartment of Radiotherapy, Shuguang Hospital, Shanghai University of Chinese Traditional MedicineDepartment of Radiotherapy, Shuguang Hospital, Shanghai University of Chinese Traditional MedicineDepartment of Radiotherapy, Shuguang Hospital, Shanghai University of Chinese Traditional MedicineDepartment of Radiotherapy, Shuguang Hospital, Shanghai University of Chinese Traditional MedicineDepartment of Radiotherapy, Shuguang Hospital, Shanghai University of Chinese Traditional MedicineDepartment of Radiotherapy, Shuguang Hospital, Shanghai University of Chinese Traditional MedicineDepartment of Radiotherapy, Shuguang Hospital, Shanghai University of Chinese Traditional MedicineDepartment of Radiotherapy, Shuguang Hospital, Shanghai University of Chinese Traditional MedicineDepartment of Radiotherapy, Changzhou Cancer HospitalAbstract Background Recent research has demonstrated that the use of artificial intelligence (AI) in radiotherapy (RT) has significantly streamlined the process for physicians to treat patients with tumors; however, bibliometric studies examining the correlation between AI and RT are not available. Providing a thorough overview of the knowledge structure and research hotspots between AI and RT was the main goal of the current study. Method A search was conducted on the Web of Science Core Collection (WoSCC) database for publications pertaining to AI and RT between 2003 and 2023. VOSviewers, CiteSpace, and the R program “bibliometrix” were used to do the bibliometric analysis. Results The analysis comprised 615 publications from 64 countries, with USA and China leading the pack. Since 2017, there have been more and more publications about RT and AI every year. The research center that made the biggest contribution to this topic was Maastricht University. The most articles published journal in this field was Frontiers in Oncology, while Medical Physics received the greatest number of citations. Dekker Andre is the author with the greatest number of published articles, while Philippe Lambin was the most often co-cited author. In the newly identified research hotspots, “autocontouring algorithm”, “deep learning”, and “machine learning” stand out as the main terms. Conclusion In fact, our bibliometric analysis offers insightful information on current research directions and advancements pertaining to the use of AI in RT. For academics looking to understand the connection between AI and RT, this study is a great resource because it highlights current research frontiers and hot trends.https://doi.org/10.1186/s13014-024-02551-1Artificial intelligenceRadiotherapyBibliometrics |
| spellingShingle | Minghe Lv Yue feng Su Zeng Yang Zhang Wenhao Shen Wenhui Guan Xiangyu E. Hongwei Zeng Ruping Zhao Jingping Yu A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023 Radiation Oncology Artificial intelligence Radiotherapy Bibliometrics |
| title | A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023 |
| title_full | A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023 |
| title_fullStr | A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023 |
| title_full_unstemmed | A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023 |
| title_short | A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023 |
| title_sort | bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023 |
| topic | Artificial intelligence Radiotherapy Bibliometrics |
| url | https://doi.org/10.1186/s13014-024-02551-1 |
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