Jan‐Niklas Eckardt
- Health Informatics top 0.2%
- Artificial Intelligence top 5%
- Radiology, Nuclear Medicine and Imaging top 5%
- Computer Vision and Pattern Recognition top 10%
- Molecular Biology
- Co-authors
- Jakob Nikolas KatherJan Moritz MiddekeMartin BornhäuserFiona R. KolbingerHannah Sophie MutiChiara Maria Lavinia LöfflerMichaela UngerGregory Patrick Veldhuizen
- Topics
- Digital Imaging for Blood Diseases (8 papers)Acute Myeloid Leukemia Research (6 papers)Artificial Intelligence in Healthcare and Education (5 papers)
- Journals
- SHILAP Revista de lepidopterologíaBloodBritish Journal of Haematology
- Partner nations
- GermanyUnited StatesSwitzerland
In The Last Decade
Jan‐Niklas Eckardt
17 papers receiving 792 citations
Hit Papers
Peers
Comparison fields: 5 of 120
- Health Informatics 370
- Artificial Intelligence 331
- Radiology, Nuclear Medicine and Imaging 234
- Computer Vision and Pattern Recognition 101
- Molecular Biology 88
Countries citing papers authored by Jan‐Niklas Eckardt
This map shows the geographic impact of Jan‐Niklas Eckardt'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 Jan‐Niklas Eckardt with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jan‐Niklas Eckardt more than expected).
Fields of papers citing papers by Jan‐Niklas Eckardt
This network shows the impact of papers produced by Jan‐Niklas Eckardt. 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 Jan‐Niklas Eckardt. The network helps show where Jan‐Niklas Eckardt may publish in the future.
Co-authorship network of co-authors of Jan‐Niklas Eckardt
This figure shows the co-authorship network connecting the top 25 collaborators of Jan‐Niklas Eckardt. A scholar is included among the top collaborators of Jan‐Niklas Eckardt based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Jan‐Niklas Eckardt. Jan‐Niklas Eckardt is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 1 | |
| 3 | 1 | |
| 4 | 0 | |
| 5 | 1 | |
| 6 | 3 | |
| 7 | 0 | |
| 8 | 2 | |
| 9 | GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelinesbreakdown → | 47 |
| 10 | 1 | |
| 11 | 0 | |
| 12 | 26 | |
| 13 | 1 | |
| 14 | 26 | |
| 15 | The future landscape of large language models in medicinebreakdown → | 472 |
| 16 | 1 | |
| 17 | 26 | |
| 18 | 41 | |
| 19 | 60 | |
| 20 | 48 |
About Jan‐Niklas Eckardt
Jan‐Niklas Eckardt is a scholar working on Health Informatics, Hematology and Computer Vision and Pattern Recognition, having authored 21 papers that have together received 810 indexed citations. Recurring topics across this work include Digital Imaging for Blood Diseases (8 papers), Acute Myeloid Leukemia Research (6 papers) and Artificial Intelligence in Healthcare and Education (5 papers). The work is most often cited by research in Health Informatics (370 citations), Family Practice (32 citations) and Health Information Management (54 citations). Jan‐Niklas Eckardt has collaborated with scholars based in Germany, United States and Switzerland. Frequent co-authors include Jakob Nikolas Kather, Jan Moritz Middeke, Martin Bornhäuser, Fiona R. Kolbinger, Hannah Sophie Muti, Chiara Maria Lavinia Löffler, Michaela Unger, Gregory Patrick Veldhuizen, Sophia J. Wagner and Narmin Ghaffari Laleh. Their work appears in journals such as SHILAP Revista de lepidopterología, Blood and British Journal of Haematology.
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.