A bibliometrics analysis based on the application of artificial intelligence in the field of radiotherapy from 2003 to 2023
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.
Authors
- Hongwei Zeng (ORCID: https://orcid.org/0000-0003-0714-5539)
- Minghe Lv (ORCID: https://orcid.org/0000-0001-9342-773X)
- E Xiangyu (ORCID: https://orcid.org/0009-0004-1922-9291)
- Ruping Zhao (ORCID: https://orcid.org/0000-0003-1096-6717)
- Jingping Yu (ORCID: https://orcid.org/0000-0003-4383-6222)
- Yang Zhang (ORCID: https://orcid.org/0000-0003-2797-7535)
- Yue Feng (ORCID: https://orcid.org/0000-0003-1477-4069)
- Su Zeng (ORCID: https://orcid.org/0000-0002-1418-6825)
- Wenhao Shen (ORCID: https://orcid.org/0000-0002-4410-128X)
- Wenhui Guan
Institutions
- Shanghai University (CN)
- Shanghai University of Traditional Chinese Medicine (CN)
- Shuguang Hospital (CN)
- Hangzhou Cancer Hospital (CN)
Publication Details
- Journal
- Radiation Oncology
- Published
- 2024-11-11
- DOI
- https://doi.org/10.1186/s13014-024-02551-1
- Citations
- 5
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.02
Funders
- Shanghai University of Traditional Chinese Medicine