Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions
Kidney transplantation is the definitive treatment for end-stage renal disease (ESRD), yet challenges persist in optimizing donor-recipient matching, postoperative care, and immunosuppressive strategies. This study employs bibliometric analysis to evaluate 890 publications from 1993 to 2023, using t...
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| Format: | Article |
| Language: | English |
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Taylor & Francis Group
2025-12-01
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| Series: | Renal Failure |
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| Online Access: | https://www.tandfonline.com/doi/10.1080/0886022X.2025.2458754 |
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| _version_ | 1832087216984162304 |
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| author | Ying Jia He Pin Lin Liu Tao Wei Tao Liu Yi Fei Li Jing Yang Wen Xing Fan |
| author_facet | Ying Jia He Pin Lin Liu Tao Wei Tao Liu Yi Fei Li Jing Yang Wen Xing Fan |
| author_sort | Ying Jia He |
| collection | DOAJ |
| description | Kidney transplantation is the definitive treatment for end-stage renal disease (ESRD), yet challenges persist in optimizing donor-recipient matching, postoperative care, and immunosuppressive strategies. This study employs bibliometric analysis to evaluate 890 publications from 1993 to 2023, using tools such as CiteSpace and VOSviewer, to identify global trends, research hotspots, and future opportunities in applying artificial intelligence (AI) to kidney transplantation. Our analysis highlights the United States as the leading contributor to the field, with significant outputs from Mayo Clinic and leading authors like Cheungpasitporn W. Key research themes include AI-driven advancements in donor matching, deep learning for post-transplant monitoring, and machine learning algorithms for personalized immunosuppressive therapies. The findings underscore a rapid expansion in AI applications since 2017, with emerging trends in personalized medicine, multimodal data fusion, and telehealth. This bibliometric review provides a comprehensive resource for researchers and clinicians, offering insights into the evolution of AI in kidney transplantation and guiding future studies toward transformative applications in transplantation science. |
| format | Article |
| id | doaj-art-462e11ec54264e039446c039ba6dcb20 |
| institution | Kabale University |
| issn | 0886-022X 1525-6049 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Renal Failure |
| spelling | doaj-art-462e11ec54264e039446c039ba6dcb202025-02-06T06:43:00ZengTaylor & Francis GroupRenal Failure0886-022X1525-60492025-12-0147110.1080/0886022X.2025.2458754Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directionsYing Jia He0Pin Lin Liu1Tao Wei2Tao Liu3Yi Fei Li4Jing Yang5Wen Xing Fan6Department of Nephrology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, ChinaDepartment of Nephrology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, ChinaDepartment of Library, Kunming Medical University, Kunming, Yunnan Province, ChinaOrgan Transplantation Center, First Affiliated Hospital, Kunming Medical University, Kunming, Yunnan Province, ChinaOrgan Transplantation Center, First Affiliated Hospital, Kunming Medical University, Kunming, Yunnan Province, ChinaDepartment of Nephrology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, ChinaDepartment of Nephrology, First Affiliated Hospital of Kunming Medical University, Kunming, Yunnan Province, ChinaKidney transplantation is the definitive treatment for end-stage renal disease (ESRD), yet challenges persist in optimizing donor-recipient matching, postoperative care, and immunosuppressive strategies. This study employs bibliometric analysis to evaluate 890 publications from 1993 to 2023, using tools such as CiteSpace and VOSviewer, to identify global trends, research hotspots, and future opportunities in applying artificial intelligence (AI) to kidney transplantation. Our analysis highlights the United States as the leading contributor to the field, with significant outputs from Mayo Clinic and leading authors like Cheungpasitporn W. Key research themes include AI-driven advancements in donor matching, deep learning for post-transplant monitoring, and machine learning algorithms for personalized immunosuppressive therapies. The findings underscore a rapid expansion in AI applications since 2017, with emerging trends in personalized medicine, multimodal data fusion, and telehealth. This bibliometric review provides a comprehensive resource for researchers and clinicians, offering insights into the evolution of AI in kidney transplantation and guiding future studies toward transformative applications in transplantation science.https://www.tandfonline.com/doi/10.1080/0886022X.2025.2458754Bibliometric analysiskidney transplantationartificial intelligenceresearch trendsresearch hotspots |
| spellingShingle | Ying Jia He Pin Lin Liu Tao Wei Tao Liu Yi Fei Li Jing Yang Wen Xing Fan Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions Renal Failure Bibliometric analysis kidney transplantation artificial intelligence research trends research hotspots |
| title | Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions |
| title_full | Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions |
| title_fullStr | Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions |
| title_full_unstemmed | Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions |
| title_short | Artificial intelligence in kidney transplantation: a 30-year bibliometric analysis of research trends, innovations, and future directions |
| title_sort | artificial intelligence in kidney transplantation a 30 year bibliometric analysis of research trends innovations and future directions |
| topic | Bibliometric analysis kidney transplantation artificial intelligence research trends research hotspots |
| url | https://www.tandfonline.com/doi/10.1080/0886022X.2025.2458754 |
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