Daniel Truhn
Impact in
- Health Informatics top 0.05%
- Artificial Intelligence in Healthcare and Education
-
- Radiomics and Machine Learning in Medical Imaging
- COVID-19 diagnosis using AI
Papers in
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- Artificial Intelligence in Healthcare and Education 35
-
- Radiomics and Machine Learning in Medical Imaging 63
- COVID-19 diagnosis using AI 13
- Co-authors
- Christiane KühlSven NebelungJakob Nikolas KatherKeno K. BressemChristoph HaarburgerDorit MerhofFelix BuschLisa C. Adams
- Journals
- Scientific Reports (17 papers)European Radiology (10 papers)Radiology (10 papers)npj Digital Medicine (7 papers)Nature Communications (5 papers)
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Daniel Truhn
138 papers receiving 2.9k citations
Hit Papers
Peers
Comparison fields: 5 of 156
- Health Informatics 708
- Radiology, Nuclear Medicine and Imaging 1.3k
- Artificial Intelligence 898
- Rheumatology 317
- Health Information Management 86
Countries citing papers authored by Daniel Truhn
This map shows the geographic impact of Daniel Truhn'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 Daniel Truhn with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Truhn more than expected).
Fields of papers citing papers by Daniel Truhn
This network shows the impact of papers produced by Daniel Truhn. 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 Daniel Truhn. The network helps show where Daniel Truhn may publish in the future.
Co-authors
The 25 scholars most cited alongside Daniel Truhn, 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 | 2025 | 3 | |
| 2 | 2025 | 3 | |
| 3 | 2025 | 0 | |
| 4 | 2024 | 47 | |
| 5 | 2024 | 11 | |
| 6 | GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines Hit paper breakdown → | 2024 | 47 |
| 7 | 2024 | 6 | |
| 8 | 2024 | 9 | |
| 9 | 2024 | 16 | |
| 10 | Denoising diffusion probabilistic models for 3D medical image generation Hit paper breakdown → | 2023 | 121 |
| 11 | 2023 | 8 | |
| 12 | 2023 | 8 | |
| 13 | 2023 | 34 | |
| 14 | 2023 | 2 | |
| 15 | 2023 | 49 | |
| 16 | 2022 | 6 | |
| 17 | 2022 | 1 | |
| 18 | 2021 | 3 | |
| 19 | 2021 | 2 | |
| 20 | 2021 | 3 |
About Daniel Truhn
Daniel Truhn is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging, Family Practice, Equine and Rheumatology, having authored 150 papers that have together received 2.9k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (63 papers), Artificial Intelligence in Healthcare and Education (35 papers), Osteoarthritis Treatment and Mechanisms (27 papers), AI in cancer detection (25 papers), Lower Extremity Biomechanics and Pathologies (16 papers), Knee injuries and reconstruction techniques (16 papers), Machine Learning in Healthcare (14 papers) and COVID-19 diagnosis using AI (13 papers). The work is most often cited by research in Health Informatics (708 citations), Radiology, Nuclear Medicine and Imaging (1.3k citations), Artificial Intelligence (898 citations), Rheumatology (317 citations) and Health Information Management (86 citations). Daniel Truhn has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Christiane Kühl, Sven Nebelung, Jakob Nikolas Kather, Keno K. Bressem, Christoph Haarburger, Dorit Merhof, Felix Busch, Lisa C. Adams, Gustav Müller‐Franzes and Tianyu Han. Their work appears in journals such as Scientific Reports, European Radiology, Radiology, npj Digital Medicine and Nature Communications.
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.