Stefan Bunk
Impact in
- Health Informatics top 2%
- Artificial Intelligence in Healthcare and Education
- Artificial Intelligence top 10%
- AI in cancer detection
- Topic Modeling
- Advanced Text Analysis Techniques
Papers in
-
- AI in cancer detection 4
- Natural Language Processing Techniques 1
- Topic Modeling 1
- Oncology 2
- Global Cancer Incidence and Screening 2
- Co-authors
- Christian Leibig (5 shared papers)Danalyn Byng (3 shared papers)Katja Pinker (1 shared paper)Lale Umutlu (1 shared paper)Ralf Krestel (1 shared paper)Trasias Mukama (2 shared papers)Nora Eisemann (2 shared papers)Susanne Elsner (2 shared papers)
- Journals
- The Lancet Digital Health (2 papers)Nature Medicine (1 paper)European Journal of Radiology (1 paper)Cancer Research (1 paper)Lecture notes in business information processing (1 paper)
- Partner nations
- GermanySwitzerlandAustria
In The Last Decade
Stefan Bunk
8 papers receiving 239 citations
Stefan Bunk's Hit Papers
Peers
Comparison fields: 5 of 48
- Health Informatics 60
- Artificial Intelligence 143
- Radiology, Nuclear Medicine and Imaging 77
- General Social Sciences 6
- Health Information Management 4
Countries citing papers authored by Stefan Bunk
This map shows the geographic impact of Stefan Bunk'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 Stefan Bunk with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Stefan Bunk more than expected).
Fields of papers citing papers by Stefan Bunk
This network shows the impact of papers produced by Stefan Bunk. 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 Stefan Bunk. The network helps show where Stefan Bunk may publish in the future.
Co-authors
The 21 scholars most cited alongside Stefan Bunk, 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 | 2022 | 114 | |
| 2 | Nationwide real-world implementation of AI for cancer detection in population-based mammography screening Hit paper breakdown → | 2025 | 71 |
| 3 | 2022 | 32 | |
| 4 | 2018 | 17 | |
| 5 | 2024 | 9 | |
| 6 | 2017 | 3 | |
| 7 | 2023 | 1 | |
| 8 | 2013 | 1 |
About Stefan Bunk
Stefan Bunk is a scholar working on Artificial Intelligence, Oncology, Radiology, Nuclear Medicine and Imaging, Management Information Systems and Information Systems, having authored 8 papers that have together received 248 indexed citations. Recurring topics across this work include AI in cancer detection (4 papers), Radiomics and Machine Learning in Medical Imaging (2 papers), Global Cancer Incidence and Screening (2 papers), Video Analysis and Summarization (1 paper), Business Process Modeling and Analysis (1 paper), Natural Language Processing Techniques (1 paper), Sports Analytics and Performance (1 paper) and Topic Modeling (1 paper). The work is most often cited by research in Health Informatics (60 citations), Artificial Intelligence (143 citations), Radiology, Nuclear Medicine and Imaging (77 citations), General Social Sciences (6 citations) and Health Information Management (4 citations). Stefan Bunk has collaborated with scholars based in Germany, Switzerland and Austria. Frequent co-authors include Christian Leibig, Danalyn Byng, Katja Pinker, Lale Umutlu, Ralf Krestel, Trasias Mukama, Nora Eisemann, Susanne Elsner, Katja Siegmann-Luz and Sylvia H. Heywang‐Köbrunner. Their work appears in journals such as The Lancet Digital Health, Nature Medicine, European Journal of Radiology, Cancer Research and Lecture notes in business information processing.
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.