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Researcher · RWTH Aachen University · Germany

Daniel Truhn

Daniel Truhn publishes mostly in Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Surgery, on topics such as Radiomics and Machine Learning in Medical Imaging, AI in cancer detection and Artificial Intelligence in Healthcare and Education.

ORCID 0000-0002-9605-0728 OpenAlex
World rank
#166,961
of 7,710,434 ranked researchers
Rank in Germany
#8,544
of 364,343
Works
491
Citations
8.5k
Citations per work
17.4
h-index
48
i10-index 147Top 2.8% in Radiology, Nuclear Medicine and Imaging
Daniel Truhn

What does Daniel Truhn research?

Daniel Truhn's work is mostly in Medicine (44%), Computer Science (18%), Engineering (7%).

Health Sciences: 216 works · 43.9% of their workMedicine: 216 works · 43.9% of their workRadiology, Nuclear Medicine and Imaging: 115 works · 23.5% of their workSurgery: 41 works · 8.4% of their workHealth Informatics: 37 works · 7.5% of their workRheumatology: 22 works · 4.5% of their workEverything else: 154 works · 31.3% of their workPhysical Sciences: 122 works · 24.8% of their workComputer Science: 88 works · 17.9% of their workArtificial Intelligence: 88 works · 17.9% of their workEngineering: 34 works · 6.9% of their workBiomedical Engineering: 34 works · 6.9% of their workTHEIR WORKDaniel Truhn491works
Physical Sciences25%Health Sciences44%

Their six largest subfields, inside their fields and domains; the grey slice is everything else they publish. The interactive view splits every one of their works down to single topics.

The fields and subfields, with their numbers
    1. Medicine21643.9%
  2. Everything else15431.3%
    1. Computer Science8817.9%
    2. Engineering346.9%
  1. 1Radiology, Nuclear Medicine and Imaging24% of their works
  2. 2Artificial Intelligence18% of their works
  3. 3Surgery8% of their works
  4. 4Health Informatics8% of their works
  5. 5Biomedical Engineering7% of their works
  6. 6Rheumatology4% of their works

Research topics

Which keywords describe Daniel Truhn's research?

The keywords of their largest research topics: Machine Learning, Medical Imaging, Precision Medicine, Predictive Modeling, Deep Learning, Quantitative Analysis, Radiomics and Convolutional Neural Networks.

Size is their works in the topics tagged with each word, from their 8 largest topics. Each links to the topic it comes from most. The words come from the keyword list OpenAlex gives each topic, not from the text of the papers, so a word can appear that none of the papers uses.

All 24 words, with their numbers
  1. Machine Learning3383 topics
  2. Medical Imaging2863 topics
  3. Precision Medicine2833 topics
  4. Predictive Modeling1882 topics
  5. Deep Learning1843 topics
  6. Quantitative Analysis1461 topic
  7. Radiomics1461 topic
  8. Convolutional Neural Networks1422 topics
  9. Artificial Intelligence1402 topics
  10. Healthcare1372 topics
  11. Histopathology Images971 topic
  12. Medical Image Analysis971 topic
  13. Clinical Decision Support951 topic
  14. Ethical Challenges951 topic
  15. Cartilage481 topic
  16. Osteoarthritis481 topic
  17. COVID-19451 topic
  18. Chest X-Ray451 topic
  19. Electronic Health Records421 topic
  20. Patient Similarity421 topic
  21. Neural Networks321 topic
  22. Word Representation321 topic
  23. Hepatocellular Carcinoma281 topic
  24. Liver Cancer281 topic

Where does Daniel Truhn work?

Daniel Truhn's main affiliation in the publication record is RWTH Aachen University, Germany.

How many publications and citations does Daniel Truhn have?

OpenAlex counts 491 works and 8,539 citations for Daniel Truhn, an h-index of 48, ranking #166,961 of 7,710,434 researchers worldwide on the quality score.

Ranked on field-normalised citation excellence (50%), output (30%) and citations (20%). Counts come from OpenAlex author records, which occasionally merge different people who share a name or split one person into several.

Papers, co-authors, who cited this work and researchers on the nearest topics have tabs of their own, above. Is this your page? Claim it with your ORCID iD, or request a correction or removal.

Discussion

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