Georgios Kaissis
- Artificial Intelligence top 1%
- Radiology, Nuclear Medicine and Imaging top 2%
- Health Informatics top 0.1%
- Oncology top 10%
- Computer Vision and Pattern Recognition top 5%
- Co-authors
- Rickmer BrarenMarcus R. MakowskiDaniel RückertDaniel RueckertFriederike JungmannDmitrii UsyninAlexander ZillerFabian Lohöfer
- Topics
- Radiomics and Machine Learning in Medical Imaging (23 papers)Privacy-Preserving Technologies in Data (18 papers)Pancreatic and Hepatic Oncology Research (14 papers)
- Journals
- Nature MedicineSHILAP Revista de lepidopterologíaPLoS ONE
- Partner nations
- GermanyUnited KingdomUnited States
In The Last Decade
Georgios Kaissis
69 papers receiving 2.1k citations
Hit Papers
Peers
Comparison fields: 5 of 153
- Artificial Intelligence 1.1k
- Radiology, Nuclear Medicine and Imaging 744
- Health Informatics 439
- Oncology 243
- Computer Vision and Pattern Recognition 187
Countries citing papers authored by Georgios Kaissis
This map shows the geographic impact of Georgios Kaissis'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 Georgios Kaissis with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Georgios Kaissis more than expected).
Fields of papers citing papers by Georgios Kaissis
This network shows the impact of papers produced by Georgios Kaissis. 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 Georgios Kaissis. The network helps show where Georgios Kaissis may publish in the future.
Co-authorship network of co-authors of Georgios Kaissis
This figure shows the co-authorship network connecting the top 25 collaborators of Georgios Kaissis. A scholar is included among the top collaborators of Georgios Kaissis 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 Georgios Kaissis. Georgios Kaissis is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 1 | |
| 2 | 1 | |
| 3 | Evaluation and mitigation of the limitations of large language models in clinical decision-makingbreakdown → | 200 |
| 4 | 16 | |
| 5 | 3 | |
| 6 | 0 | |
| 7 | 3 | |
| 8 | 8 | |
| 9 | 33 | |
| 10 | 3 | |
| 11 | End-to-end privacy preserving deep learning on multi-institutional medical imagingbreakdown → | 255 |
| 12 | 14 | |
| 13 | 18 | |
| 14 | 87 | |
| 15 | 48 | |
| 16 | 10 | |
| 17 | 11 | |
| 18 | 29 | |
| 19 | 18 | |
| 20 | 13 |
About Georgios Kaissis
Georgios Kaissis is a scholar working on Health Informatics, Radiology, Nuclear Medicine and Imaging and Artificial Intelligence, having authored 74 papers that have together received 2.1k indexed citations. Recurring topics across this work include Radiomics and Machine Learning in Medical Imaging (23 papers), Privacy-Preserving Technologies in Data (18 papers) and Pancreatic and Hepatic Oncology Research (14 papers). The work is most often cited by research in Health Informatics (439 citations), Artificial Intelligence (1.1k citations) and Radiology, Nuclear Medicine and Imaging (744 citations). Georgios Kaissis has collaborated with scholars based in Germany, United Kingdom and United States. Frequent co-authors include Rickmer Braren, Marcus R. Makowski, Daniel Rückert, Daniel Rueckert, Friederike Jungmann, Dmitrii Usynin, Alexander Ziller, Fabian Lohöfer, Sebastian Ziegelmayer and Jonathan Passerat‐Palmbach. Their work appears in journals such as Nature Medicine, SHILAP Revista de lepidopterología and PLoS ONE.
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.