Markus Knott
Impact in
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- Radiopharmaceutical Chemistry and Applications
- Medical Imaging Techniques and Applications
- Hematology top 10%
- Multiple Myeloma Research and Treatments
Papers in
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- Artificial Intelligence in Healthcare and Education 3
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- Machine Learning in Healthcare 3
- Co-authors
- Samuel Samnick (2 shared papers)Martin Schreder (2 shared papers)Katharina Lückerath (2 shared papers)Constantin Lapa (3 shared papers)Andreas Konrad Buck (3 shared papers)Ken Herrmann (3 shared papers)Stefan Tobias Knop (2 shared papers)Hermann Einsele (2 shared papers)
- Journals
- Journal of Medical Internet Research (1 paper)European Journal Of Haematology (1 paper)PLoS ONE (1 paper)Immunology (1 paper)Theranostics (1 paper)
- Partner nations
- GermanyUnited States
In The Last Decade
Markus Knott
8 papers receiving 322 citations
Peers
Comparison fields: 5 of 35
- Radiology, Nuclear Medicine and Imaging 178
- Hematology 107
- Oncology 34
- Pathology and Forensic Medicine 16
- Health Informatics 2
Countries citing papers authored by Markus Knott
This map shows the geographic impact of Markus Knott'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 Markus Knott with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Markus Knott more than expected).
Fields of papers citing papers by Markus Knott
This network shows the impact of papers produced by Markus Knott. 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 Markus Knott. The network helps show where Markus Knott may publish in the future.
Co-authors
The 25 scholars most cited alongside Markus Knott, 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 | 2015 | 222 | |
| 2 | 2015 | 69 | |
| 3 | 2011 | 15 | |
| 4 | 2014 | 8 | |
| 5 | 2014 | 3 | |
| 6 | 2024 | 3 | |
| 7 | 2025 | 2 | |
| 8 | 2026 | 1 | |
| 9 | 2026 | 0 |
About Markus Knott
Markus Knott is a scholar working on Health Informatics, Artificial Intelligence, Health Information Management, Hematology and Pathology and Forensic Medicine, having authored 9 papers that have together received 323 indexed citations. Recurring topics across this work include Artificial Intelligence in Healthcare and Education (3 papers), Machine Learning in Healthcare (3 papers), Biomedical Text Mining and Ontologies (2 papers), Radiopharmaceutical Chemistry and Applications (1 paper), Lymphoma Diagnosis and Treatment (1 paper), T-cell and B-cell Immunology (1 paper), Artificial Intelligence in Healthcare (1 paper) and Multiple Myeloma Research and Treatments (1 paper). The work is most often cited by research in Radiology, Nuclear Medicine and Imaging (178 citations), Hematology (107 citations), Oncology (34 citations), Pathology and Forensic Medicine (16 citations) and Health Informatics (2 citations). Markus Knott has collaborated with scholars based in Germany and United States. Frequent co-authors include Samuel Samnick, Martin Schreder, Katharina Lückerath, Constantin Lapa, Andreas Konrad Buck, Ken Herrmann, Stefan Tobias Knop, Hermann Einsele, Christina Bluemel and Theresa Osl. Their work appears in journals such as Journal of Medical Internet Research, European Journal Of Haematology, PLoS ONE, Immunology and Theranostics.
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.