Jan van Zelst
Impact in
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- MRI in cancer diagnosis
- Radiomics and Machine Learning in Medical Imaging
- Advanced MRI Techniques and Applications
- Pathology and Forensic Medicine top 10%
- Breast Lesions and Carcinomas
Papers in
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- AI in cancer detection 17
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- Breast Lesions and Carcinomas 16
- Co-authors
- Ritse M. Mann (21 shared papers)Nico Karssemeijer (16 shared papers)Bram Platel (7 shared papers)Roel Mus (3 shared papers)Christian Geppert (1 shared paper)Suzan Vreemann (6 shared papers)Tao Tan (8 shared papers)Albert Gubern‐Mérida (6 shared papers)
- Journals
- Investigative Radiology (3 papers)Medical Physics (3 papers)European Radiology (2 papers)European Journal of Radiology (2 papers)Academic Radiology (1 paper)
- Partner nations
- NetherlandsUnited StatesBelgium
In The Last Decade
Jan van Zelst
21 papers receiving 709 citations
Peers
Comparison fields: 5 of 54
- Radiology, Nuclear Medicine and Imaging 473
- Pathology and Forensic Medicine 198
- Artificial Intelligence 311
- Health Informatics 9
- Pulmonary and Respiratory Medicine 195
Countries citing papers authored by Jan van Zelst
This map shows the geographic impact of Jan van Zelst'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 Jan van Zelst with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan van Zelst more than expected).
Fields of papers citing papers by Jan van Zelst
This network shows the impact of papers produced by Jan van Zelst. 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 Jan van Zelst. The network helps show where Jan van Zelst may publish in the future.
Co-authors
The 25 scholars most cited alongside Jan van Zelst, 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 23 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2014 | 164 | |
| 2 | 2018 | 64 | |
| 3 | 2018 | 57 | |
| 4 | 2018 | 55 | |
| 5 | 2017 | 51 | |
| 6 | 2017 | 48 | |
| 7 | 2017 | 45 | |
| 8 | 2015 | 38 | |
| 9 | 2015 | 33 | |
| 10 | 2018 | 30 | |
| 11 | 2014 | 22 | |
| 12 | 2019 | 19 | |
| 13 | 2016 | 18 | |
| 14 | 2019 | 18 | |
| 15 | 2016 | 15 | |
| 16 | 2016 | 14 | |
| 17 | 2017 | 11 | |
| 18 | 2017 | 9 | |
| 19 | 2017 | 8 | |
| 20 | 2020 | 2 |
About Jan van Zelst
Jan van Zelst is a scholar working on Artificial Intelligence, Pathology and Forensic Medicine, Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging and Cancer Research, having authored 23 papers that have together received 722 indexed citations. Recurring topics across this work include AI in cancer detection (17 papers), Breast Lesions and Carcinomas (16 papers), Digital Radiography and Breast Imaging (9 papers), MRI in cancer diagnosis (5 papers), Breast Cancer Treatment Studies (4 papers), Advanced MRI Techniques and Applications (4 papers), Global Cancer Incidence and Screening (3 papers) and Radiomics and Machine Learning in Medical Imaging (3 papers). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (473 citations), Pathology and Forensic Medicine (198 citations), Artificial Intelligence (311 citations), Health Informatics (9 citations) and Pulmonary and Respiratory Medicine (195 citations). Jan van Zelst has collaborated with scholars based in Netherlands, United States and Belgium. Frequent co-authors include Ritse M. Mann, Nico Karssemeijer, Bram Platel, Roel Mus, Christian Geppert, Suzan Vreemann, Tao Tan, Albert Gubern‐Mérida, Peter Bult and Matthieu Rutten. Their work appears in journals such as Investigative Radiology, Medical Physics, European Radiology, European Journal of Radiology and Academic Radiology.
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.