Xun Ding
Impact in
-
- Ultrasound in Clinical Applications
- Infectious Diseases top 10%
- COVID-19 Clinical Research Studies
- SARS-CoV-2 and COVID-19 Research
Papers in
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- COVID-19 Clinical Research Studies 4
- SARS-CoV-2 and COVID-19 Research 3
- SARS-CoV-2 detection and testing 2
- Co-authors
- Jun ZhouQingyun LongXu JiaShanshan YuBing LiuJiong YangHaibo XuXiaolong Zhang
- Journals
- Medicine (2 papers)European Journal of Radiology (2 papers)iScience (1 paper)The Thoracic and Cardiovascular Surgeon (1 paper)The American Journal of Emergency Medicine (1 paper)
- Partner nations
- ChinaUnited StatesSlovenia
In The Last Decade
Xun Ding
15 papers receiving 278 citations
Peers
Comparison fields: 5 of 63
- Critical Care and Intensive Care Medicine 39
- Infectious Diseases 123
- Radiology, Nuclear Medicine and Imaging 121
- Health Informatics 6
- Neurology 65
Countries citing papers authored by Xun Ding
This map shows the geographic impact of Xun Ding's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Xun Ding with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xun Ding more than expected).
Fields of papers citing papers by Xun Ding
This network shows the impact of papers produced by Xun Ding. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Xun Ding. The network helps show where Xun Ding may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Xun Ding, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2025 | 0 | |
| 2 | 2025 | 2 | |
| 3 | 2024 | 0 | |
| 4 | 2023 | 1 | |
| 5 | 2021 | 6 | |
| 6 | 2021 | 3 | |
| 7 | 2021 | 3 | |
| 8 | 2021 | 1 | |
| 9 | 2021 | 1 | |
| 10 | 2020 | 11 | |
| 11 | 2020 | 179 | |
| 12 | 2020 | 36 | |
| 13 | 2020 | 10 | |
| 14 | 2019 | 6 | |
| 15 | 2017 | 2 | |
| 16 | 2016 | 17 | |
| 17 | 2015 | 0 | |
| 18 | 2014 | 6 |
About Xun Ding
Xun Ding is a scholar working on Infectious Diseases, Critical Care and Intensive Care Medicine, Hepatology, Radiology, Nuclear Medicine and Imaging and Genetics, having authored 18 papers that have together received 284 indexed citations. Recurring topics across this work include COVID-19 Clinical Research Studies (4 papers), COVID-19 diagnosis using AI (4 papers), SARS-CoV-2 and COVID-19 Research (3 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), MRI in cancer diagnosis (2 papers), Hepatocellular Carcinoma Treatment and Prognosis (2 papers), SARS-CoV-2 detection and testing (2 papers) and Advanced MRI Techniques and Applications (2 papers). The work is most often cited by research in Critical Care and Intensive Care Medicine (39 citations), Infectious Diseases (123 citations), Radiology, Nuclear Medicine and Imaging (121 citations), Health Informatics (6 citations) and Neurology (65 citations). Xun Ding has collaborated with scholars based in China, United States and Slovenia. Frequent co-authors include Jun Zhou, Qingyun Long, Xu Jia, Shanshan Yu, Bing Liu, Jiong Yang, Haibo Xu, Jun Zhou, Xiaolong Zhang and Wei Zhou. Their work appears in journals such as Medicine, European Journal of Radiology, iScience, The Thoracic and Cardiovascular Surgeon and The American Journal of Emergency Medicine.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.