Philipp Keyl
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- Radiomics and Machine Learning in Medical Imaging
Papers in
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- Radiomics and Machine Learning in Medical Imaging 3
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- Computational Drug Discovery Methods 2
- Co-authors
- Frederick Klauschen (5 shared papers)Michael Ludwig Bockmayr (4 shared papers)Grégoire Montavon (4 shared papers)Klaus- Robert Müller (4 shared papers)Maximilian Alber (1 shared paper)Andreas Möck (2 shared papers)Lukas Ruff (1 shared paper)Philipp Sebastian Jurmeister (2 shared papers)
- Journals
- Nature Communications (2 papers)Annual Review of Pathology Mechanisms of Disease (1 paper)European Urology Oncology (1 paper)Nature Cancer (1 paper)Nucleic Acids Research (1 paper)
- Partner nations
- GermanySouth KoreaCanada
In The Last Decade
Philipp Keyl
7 papers receiving 232 citations
Peers
Comparison fields: 5 of 55
- Health Informatics 27
- Radiology, Nuclear Medicine and Imaging 44
- Artificial Intelligence 62
- Biophysics 10
- Cancer Research 23
Countries citing papers authored by Philipp Keyl
This map shows the geographic impact of Philipp Keyl'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 Philipp Keyl with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philipp Keyl more than expected).
Fields of papers citing papers by Philipp Keyl
This network shows the impact of papers produced by Philipp Keyl. 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 Philipp Keyl. The network helps show where Philipp Keyl may publish in the future.
Co-authors
The 22 scholars most cited alongside Philipp Keyl, 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 | 88 | |
| 2 | 2022 | 59 | |
| 3 | 2023 | 38 | |
| 4 | 2025 | 28 | |
| 5 | 2022 | 23 | |
| 6 | 2025 | 3 | |
| 7 | 2025 | 2 | |
| 8 | 2025 | 0 | |
| 9 | 2025 | 0 |
About Philipp Keyl
Philipp Keyl is a scholar working on Radiology, Nuclear Medicine and Imaging, Computational Theory and Mathematics, Health Informatics, Biophysics and Artificial Intelligence, having authored 9 papers that have together received 241 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (3 papers), Computational Drug Discovery Methods (2 papers), AI in cancer detection (2 papers), Gene expression and cancer classification (1 paper), Gene Regulatory Network Analysis (1 paper), Salivary Gland Tumors Diagnosis and Treatment (1 paper), Chromatin Remodeling and Cancer (1 paper) and Bioinformatics and Genomic Networks (1 paper). The work is most often cited by research in Health Informatics (27 citations), Radiology, Nuclear Medicine and Imaging (44 citations), Artificial Intelligence (62 citations), Biophysics (10 citations) and Cancer Research (23 citations). Philipp Keyl has collaborated with scholars based in Germany, South Korea and Canada. Frequent co-authors include Frederick Klauschen, Michael Ludwig Bockmayr, Grégoire Montavon, Klaus- Robert Müller, Maximilian Alber, Andreas Möck, Lukas Ruff, Philipp Sebastian Jurmeister, Philip Bischoff and Nils Blüthgen. Their work appears in journals such as Nature Communications, Annual Review of Pathology Mechanisms of Disease, European Urology Oncology, Nature Cancer and Nucleic Acids Research.
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.