Artificial intelligence in precision medicine for lung cancer: A bibliometric analysis
Background The increasing body of evidence has been stimulating the application of artificial intelligence (AI) in precision medicine research for lung cancer. This trend necessitates a comprehensive overview of the growing number of publications to facilitate researchers’ understanding of this fiel...
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| Format: | Article |
| Language: | English |
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SAGE Publishing
2025-01-01
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| Series: | Digital Health |
| Online Access: | https://doi.org/10.1177/20552076241300229 |
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| _version_ | 1841560893129752576 |
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| author | Yuchai Wang Weilong Zhang Xiang Liu Li Tian Wenjiao Li Peng He Sheng Huang Fuyuan He Xue Pan |
| author_facet | Yuchai Wang Weilong Zhang Xiang Liu Li Tian Wenjiao Li Peng He Sheng Huang Fuyuan He Xue Pan |
| author_sort | Yuchai Wang |
| collection | DOAJ |
| description | Background The increasing body of evidence has been stimulating the application of artificial intelligence (AI) in precision medicine research for lung cancer. This trend necessitates a comprehensive overview of the growing number of publications to facilitate researchers’ understanding of this field. Method The bibliometric data for the current analysis was extracted from the Web of Science Core Collection database, CiteSpace, VOSviewer ,and an online website were applied to the analysis. Results After the data were filtered, this search yielded 4062 manuscripts. And 92.27% of the papers were published from 2014 onwards. The main contributing countries were China, the United States, India, Japan, and Korea. These publications were mainly published in the following scientific disciplines, including Radiology Nuclear Medicine, Medical Imaging, Oncology, and Computer Science Notably, Li Weimin and Aerts Hugo J. W. L. stand out as leading authorities in this domain. In the keyword co-occurrence and co-citation cluster analysis of the publication, the knowledge base was divided into four clusters that are more easily understood, including screening, diagnosis, treatment, and prognosis. Conclusion This bibliometric study reveals deep learning frameworks and AI-based radiomics are receiving attention. High-quality and standardized data have the potential to revolutionize lung cancer screening and diagnosis in the era of precision medicine. However, the importance of high-quality clinical datasets, the development of new and combined AI models, and their consistent assessment for advancing research on AI applications in lung cancer are highlighted before current research can be effectively applied in clinical practice. |
| format | Article |
| id | doaj-art-5a64f361ee1949cc80acc8b7de016ce9 |
| institution | Kabale University |
| issn | 2055-2076 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SAGE Publishing |
| record_format | Article |
| series | Digital Health |
| spelling | doaj-art-5a64f361ee1949cc80acc8b7de016ce92025-01-03T10:03:36ZengSAGE PublishingDigital Health2055-20762025-01-011110.1177/20552076241300229Artificial intelligence in precision medicine for lung cancer: A bibliometric analysisYuchai Wang0Weilong Zhang1Xiang Liu2Li Tian3Wenjiao Li4Peng He5Sheng Huang6Fuyuan He7Xue Pan8 Department of Pharmacy, , Changsha, Hunan Province, China Department of Pharmacy, , Changsha, Hunan Province, China Department of Pharmacy, , Changsha, Hunan Province, China Department of Pharmacy, , Changsha, Hunan Province, China Department of Pharmacy, , Changsha, Hunan Province, China Department of Pharmacy, , Changsha, Hunan Province, China Jiuzhitang Co., Ltd, Changsha, Hunan Province, China School of Pharmacy, Hunan University of Chinese Medicine, Changsha, Hunan Province, China School of Pharmacy, Hunan University of Chinese Medicine, Changsha, Hunan Province, ChinaBackground The increasing body of evidence has been stimulating the application of artificial intelligence (AI) in precision medicine research for lung cancer. This trend necessitates a comprehensive overview of the growing number of publications to facilitate researchers’ understanding of this field. Method The bibliometric data for the current analysis was extracted from the Web of Science Core Collection database, CiteSpace, VOSviewer ,and an online website were applied to the analysis. Results After the data were filtered, this search yielded 4062 manuscripts. And 92.27% of the papers were published from 2014 onwards. The main contributing countries were China, the United States, India, Japan, and Korea. These publications were mainly published in the following scientific disciplines, including Radiology Nuclear Medicine, Medical Imaging, Oncology, and Computer Science Notably, Li Weimin and Aerts Hugo J. W. L. stand out as leading authorities in this domain. In the keyword co-occurrence and co-citation cluster analysis of the publication, the knowledge base was divided into four clusters that are more easily understood, including screening, diagnosis, treatment, and prognosis. Conclusion This bibliometric study reveals deep learning frameworks and AI-based radiomics are receiving attention. High-quality and standardized data have the potential to revolutionize lung cancer screening and diagnosis in the era of precision medicine. However, the importance of high-quality clinical datasets, the development of new and combined AI models, and their consistent assessment for advancing research on AI applications in lung cancer are highlighted before current research can be effectively applied in clinical practice.https://doi.org/10.1177/20552076241300229 |
| spellingShingle | Yuchai Wang Weilong Zhang Xiang Liu Li Tian Wenjiao Li Peng He Sheng Huang Fuyuan He Xue Pan Artificial intelligence in precision medicine for lung cancer: A bibliometric analysis Digital Health |
| title | Artificial intelligence in precision medicine for lung cancer: A bibliometric analysis |
| title_full | Artificial intelligence in precision medicine for lung cancer: A bibliometric analysis |
| title_fullStr | Artificial intelligence in precision medicine for lung cancer: A bibliometric analysis |
| title_full_unstemmed | Artificial intelligence in precision medicine for lung cancer: A bibliometric analysis |
| title_short | Artificial intelligence in precision medicine for lung cancer: A bibliometric analysis |
| title_sort | artificial intelligence in precision medicine for lung cancer a bibliometric analysis |
| url | https://doi.org/10.1177/20552076241300229 |
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