Xin Tie
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
- Medical Imaging Techniques and Applications
Papers in
-
- Radiomics and Machine Learning in Medical Imaging 8
- Medical Imaging Techniques and Applications 5
- COVID-19 diagnosis using AI 3
-
- Sepsis Diagnosis and Treatment 3
- Co-authors
- Kwok‐Hung Au (1 shared paper)Saikit Lam (1 shared paper)Yong Zhang (1 shared paper)Jing Cai (1 shared paper)Guang‐Hong Chen (4 shared papers)Zhihua Qi (2 shared papers)John W. Garrett (3 shared papers)Chengzhu Zhang (2 shared papers)
- Journals
- Medical Physics (2 papers)Frontiers in Medicine (2 papers)Radiology Artificial Intelligence (1 paper)Scientific Reports (1 paper)RSC Advances (1 paper)
- Partner nations
- ChinaUnited StatesSouth Korea
In The Last Decade
Xin Tie
19 papers receiving 258 citations
Peers
Comparison fields: 5 of 57
- Health Informatics 58
- Radiology, Nuclear Medicine and Imaging 184
- Radiation 30
- Health Information Management 9
- Artificial Intelligence 57
Countries citing papers authored by Xin Tie
This map shows the geographic impact of Xin Tie'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 Xin Tie with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xin Tie more than expected).
Fields of papers citing papers by Xin Tie
This network shows the impact of papers produced by Xin Tie. 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 Xin Tie. The network helps show where Xin Tie may publish in the future.
Co-authors
The 25 scholars most cited alongside Xin Tie, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 95 | |
| 2 | 2020 | 57 | |
| 3 | 2024 | 20 | |
| 4 | 2024 | 18 | |
| 5 | 2020 | 12 | |
| 6 | 2024 | 11 | |
| 7 | 2024 | 10 | |
| 8 | 2025 | 9 | |
| 9 | 2021 | 9 | |
| 10 | 2021 | 5 | |
| 11 | 2023 | 4 | |
| 12 | 2023 | 4 | |
| 13 | 2025 | 3 | |
| 14 | 2023 | 2 | |
| 15 | 2024 | 1 | |
| 16 | 2025 | 1 | |
| 17 | 2024 | 1 | |
| 18 | 2024 | 1 | |
| 19 | 2022 | 1 | |
| 20 | 2025 | 0 |
About Xin Tie
Xin Tie is a scholar working on Radiology, Nuclear Medicine and Imaging, Epidemiology, Artificial Intelligence, Surgery and Molecular Biology, having authored 24 papers that have together received 264 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (8 papers), Medical Imaging Techniques and Applications (5 papers), COVID-19 diagnosis using AI (3 papers), Hemodynamic Monitoring and Therapy (3 papers), Advanced Radiotherapy Techniques (3 papers), Sepsis Diagnosis and Treatment (3 papers), Glutathione Transferases and Polymorphisms (2 papers) and Advanced X-ray and CT Imaging (2 papers). The work is most often cited by research in Health Informatics (58 citations), Radiology, Nuclear Medicine and Imaging (184 citations), Radiation (30 citations), Health Information Management (9 citations) and Artificial Intelligence (57 citations). Xin Tie has collaborated with scholars based in China, United States and South Korea. Frequent co-authors include Kwok‐Hung Au, Saikit Lam, Yong Zhang, Jing Cai, Guang‐Hong Chen, Zhihua Qi, John W. Garrett, Chengzhu Zhang, Tyler Bradshaw and Ke Li. Their work appears in journals such as Medical Physics, Frontiers in Medicine, Radiology Artificial Intelligence, Scientific Reports and RSC Advances.
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.