Roman D. Buelow
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education 2
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- Radiomics and Machine Learning in Medical Imaging 3
- Artificial Intelligence top 10%
- AI in cancer detection 3
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- Genetic factors in colorectal cancer 2
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- Bacillus and Francisella bacterial research 1
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- Generative Adversarial Networks and Image Synthesis 1
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- Hepatocellular Carcinoma Treatment and Prognosis 1
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- Transplantation: Methods and Outcomes 1
- Co-authors
- Peter BoorJakob Nikolas KatherNarmin Ghaffari LalehAmelie EchleHeike I. GrabschTitus J. BrinkerMarko van TreeckPhilip Quirke
- Journals
- Nature Communications (1 paper)SHILAP Revista de lepidopterología (1 paper)Journal of the American College of Cardiology (1 paper)
- Partner nations
- GermanyNetherlandsUnited Kingdom
In The Last Decade
Roman D. Buelow
8 papers receiving 189 citations
Peers
Comparison fields: 5 of 54
- Health Informatics 26
- Radiology, Nuclear Medicine and Imaging 85
- Artificial Intelligence 109
- Biophysics 17
- Cancer Research 28
Countries citing papers authored by Roman D. Buelow
This map shows the geographic impact of Roman D. Buelow'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 Roman D. Buelow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Roman D. Buelow more than expected).
Fields of papers citing papers by Roman D. Buelow
This network shows the impact of papers produced by Roman D. Buelow. 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 Roman D. Buelow. The network helps show where Roman D. Buelow may publish in the future.
Co-authorship network
The 25 scholars most cited alongside Roman D. Buelow, 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 | 2023 | 21 | |
| 2 | 2022 | 2 | |
| 3 | 2022 | 10 | |
| 4 | 2022 | 53 | |
| 5 | 2022 | 6 | |
| 6 | 2021 | 51 | |
| 7 | 2021 | 33 | |
| 8 | 2020 | 16 |
About Roman D. Buelow
Roman D. Buelow is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Microbiology, having authored 8 papers that have together received 192 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (3 papers), AI in cancer detection (3 papers), Artificial Intelligence in Healthcare and Education (2 papers), Genetic factors in colorectal cancer (2 papers), Bacillus and Francisella bacterial research (1 paper), Generative Adversarial Networks and Image Synthesis (1 paper), Hepatocellular Carcinoma Treatment and Prognosis (1 paper) and Transplantation: Methods and Outcomes (1 paper). The work is most often cited by research in Health Informatics (26 citations), Radiology, Nuclear Medicine and Imaging (85 citations) and Artificial Intelligence (109 citations). Roman D. Buelow has collaborated with scholars based in Germany, Netherlands and United Kingdom. Frequent co-authors include Peter Boor, Jakob Nikolas Kather, Narmin Ghaffari Laleh, Amelie Echle, Heike I. Grabsch, Titus J. Brinker, Marko van Treeck, Philip Quirke, Christian Trautwein and Volkmar Schulz. Their work appears in journals such as Nature Communications, SHILAP Revista de lepidopterología and Journal of the American College of Cardiology.
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.