Tassilo Wald
Impact in
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- Artificial Intelligence in Healthcare
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- Phonocardiography and Auscultation Techniques
- Chronic Obstructive Pulmonary Disease (COPD) Research
- Respiratory Support and Mechanisms
Papers in
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- Chronic Obstructive Pulmonary Disease (COPD) Research 1
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- Machine Learning and Data Classification 1
- Co-authors
- Oyunbileg von Stackelberg (3 shared papers)Oliver Weinheimer (3 shared papers)Jürgen Biederer (3 shared papers)Klaus Maier‐Hein (5 shared papers)Vivienn Weru (3 shared papers)Tobias Norajitra (3 shared papers)Paul F. Jäger (3 shared papers)Marco Nolden (3 shared papers)
- Journals
- European Radiology Experimental (1 paper)European Radiology (1 paper)Frontiers in Medicine (1 paper)Insights into Imaging (1 paper)FreiDok plus (Universitätsbibliothek Freiburg) (1 paper)
- Partner nations
- GermanyUnited StatesLatvia
In The Last Decade
Tassilo Wald
5 papers receiving 26 citations
Peers
Comparison fields: 5 of 12
- Health Information Management 6
- Pulmonary and Respiratory Medicine 15
- Radiology, Nuclear Medicine and Imaging 9
- Complementary and alternative medicine 2
- Industrial and Manufacturing Engineering 1
Countries citing papers authored by Tassilo Wald
This map shows the geographic impact of Tassilo Wald'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 Tassilo Wald with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Tassilo Wald more than expected).
Fields of papers citing papers by Tassilo Wald
This network shows the impact of papers produced by Tassilo Wald. 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 Tassilo Wald. The network helps show where Tassilo Wald may publish in the future.
Co-authors
The 25 scholars most cited alongside Tassilo Wald, 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 | 14 | |
| 2 | 2024 | 5 | |
| 3 | 2024 | 5 | |
| 4 | 2025 | 1 | |
| 5 | 2024 | 1 | |
| 6 | 2025 | 0 |
About Tassilo Wald
Tassilo Wald is a scholar working on Pulmonary and Respiratory Medicine, Artificial Intelligence, Radiology, Nuclear Medicine and Imaging, Infectious Diseases and Organic Chemistry, having authored 6 papers that have together received 26 indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (1 paper), Machine Learning and Data Classification (1 paper) and Chronic Obstructive Pulmonary Disease (COPD) Research (1 paper). The work is most often cited by research in Health Information Management (6 citations), Pulmonary and Respiratory Medicine (15 citations), Radiology, Nuclear Medicine and Imaging (9 citations), Complementary and alternative medicine (2 citations) and Industrial and Manufacturing Engineering (1 citation). Tassilo Wald has collaborated with scholars based in Germany, United States and Latvia. Frequent co-authors include Oyunbileg von Stackelberg, Oliver Weinheimer, Jürgen Biederer, Klaus Maier‐Hein, Vivienn Weru, Tobias Norajitra, Paul F. Jäger, Marco Nolden, Hans‐Ulrich Kauczor and Claus Peter Heußel. Their work appears in journals such as European Radiology Experimental, European Radiology, Frontiers in Medicine, Insights into Imaging and FreiDok plus (Universitätsbibliothek Freiburg).
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.