Jakob Nikolas Kather

19.0k total citations · 14 hit papers
214 papers, 7.7k citations indexed

About

Jakob Nikolas Kather is a scholar working on Radiology, Nuclear Medicine and Imaging, Artificial Intelligence and Oncology. According to data from OpenAlex, Jakob Nikolas Kather has authored 214 papers receiving a total of 7.7k indexed citations (citations by other indexed papers that have themselves been cited), including 108 papers in Radiology, Nuclear Medicine and Imaging, 99 papers in Artificial Intelligence and 71 papers in Oncology. Recurrent topics in Jakob Nikolas Kather's work include Radiomics and Machine Learning in Medical Imaging (95 papers), AI in cancer detection (75 papers) and Artificial Intelligence in Healthcare and Education (44 papers). Jakob Nikolas Kather is often cited by papers focused on Radiomics and Machine Learning in Medical Imaging (95 papers), AI in cancer detection (75 papers) and Artificial Intelligence in Healthcare and Education (44 papers). Jakob Nikolas Kather collaborates with scholars based in Germany, United Kingdom and United States. Jakob Nikolas Kather's co-authors include Narmin Ghaffari Laleh, Niels Halama, Tom Luedde, Alexander Marx, Alexander T. Pearson, Daniel Truhn, Michael Hoffmeister, Hermann Brenner, Cleo‐Aron Weis and Titus J. Brinker and has published in prestigious journals such as Nature Medicine, Nature Communications and Journal of Clinical Oncology.

In The Last Decade

Jakob Nikolas Kather

193 papers receiving 7.5k citations

Hit Papers

Deep learning can predict microsatellite instability dire... 2016 2026 2019 2022 2019 2019 2023 2020 2016 250 500 750
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

  1. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer
    2019 798 citations Jakob Nikolas Kather, Niels Halama et al. profile →
  2. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
    2019 611 citations Jakob Nikolas Kather, Johannes Krisam et al. PLoS Medicine profile →
  3. The future landscape of large language models in medicine
    2023 472 citations Zunamys I. Carrero, Michaela Unger et al. profile →
  4. Deep learning in cancer pathology: a new generation of clinical biomarkers
    2020 362 citations Amelie Echle, Titus J. Brinker et al. profile →
  5. Multi-class texture analysis in colorectal cancer histology
    2016 326 citations Jakob Nikolas Kather, Cleo‐Aron Weis et al. Scientific Reports profile →
  6. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology
    2022 239 citations Jakob Nikolas Kather et al. profile →
  7. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer
    2023 141 citations Sebastian Foersch, Wilfried Roth et al. profile →
  8. Denoising diffusion probabilistic models for 3D medical image generation
    2023 121 citations Firas Khader, Gustav Müller‐Franzes et al. Scientific Reports profile →
  9. Artificial intelligence in liver diseases: Improving diagnostics, prognostics and response prediction
    2022 119 citations Jakob Nikolas Kather et al. profile →
  10. A guide to artificial intelligence for cancer researchers
    2024 89 citations Jakob Nikolas Kather et al. profile →
  11. Current applications and challenges in large language models for patient care: a systematic review
    2025 58 citations Felix Busch, Lena Hoffmann et al. Communications Medicine profile →
  12. In-context learning enables multimodal large language models to classify cancer pathology images
    2024 47 citations Dyke Ferber, Georg Wölflein et al. profile →
  13. GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines
    2024 47 citations Dyke Ferber, Isabella C. Wiest et al. NEJM AI profile →
  14. Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology
    2025 18 citations Dyke Ferber, Omar S. M. El Nahhas et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Jakob Nikolas Kather Germany 44 3.3k 3.3k 2.1k 1.2k 1.1k 214 7.7k
Justin Ko United States 22 3.7k 1.1× 2.6k 0.8× 2.3k 1.1× 1.3k 1.1× 197 0.2× 89 9.8k
Daniel L. Rubin United States 63 5.9k 1.8× 8.2k 2.5× 1.3k 0.6× 1.2k 1.0× 766 0.7× 366 15.9k
Alexander T. Pearson United States 32 1.2k 0.4× 1.2k 0.4× 1.3k 0.6× 549 0.5× 677 0.6× 159 4.1k
Dong Ni China 43 2.5k 0.7× 2.2k 0.7× 701 0.3× 287 0.2× 391 0.4× 232 6.8k
Ritse M. Mann Netherlands 49 3.8k 1.1× 6.5k 1.9× 1.3k 0.6× 687 0.6× 1.3k 1.2× 215 9.1k
Frederick Klauschen Germany 46 2.1k 0.6× 881 0.3× 2.6k 1.3× 268 0.2× 1.5k 1.4× 201 10.5k
Nico Karssemeijer Netherlands 53 5.6k 1.7× 4.9k 1.5× 1.7k 0.8× 314 0.3× 597 0.5× 200 8.6k
Yun Liu United States 29 2.0k 0.6× 2.4k 0.7× 436 0.2× 877 0.7× 219 0.2× 112 5.4k
Hannah Gilmore United States 33 2.3k 0.7× 2.3k 0.7× 1.4k 0.7× 118 0.1× 970 0.9× 78 5.4k
David Snead United Kingdom 39 2.4k 0.7× 2.3k 0.7× 980 0.5× 181 0.1× 420 0.4× 128 6.6k

Countries citing papers authored by Jakob Nikolas Kather

Since Specialization
Citations

This map shows the geographic impact of Jakob Nikolas Kather'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 Jakob Nikolas Kather with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Jakob Nikolas Kather more than expected).

Fields of papers citing papers by Jakob Nikolas Kather

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Jakob Nikolas Kather. 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 Jakob Nikolas Kather. The network helps show where Jakob Nikolas Kather may publish in the future.

Co-authorship network of co-authors of Jakob Nikolas Kather

This figure shows the co-authorship network connecting the top 25 collaborators of Jakob Nikolas Kather. A scholar is included among the top collaborators of Jakob Nikolas Kather 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 Jakob Nikolas Kather. Jakob Nikolas Kather is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Busch, Felix, Lena Hoffmann, Elon H. C. van Dijk, et al.. (2025). Current applications and challenges in large language models for patient care: a systematic review. Communications Medicine. 5(1). 26–26. 58 indexed citations breakdown →
2.
Ligero, Marta, Omar S. M. El Nahhas, Mihaela Aldea, & Jakob Nikolas Kather. (2025). Artificial intelligence-based biomarkers for treatment decisions in oncology. Trends in cancer. 11(3). 232–244. 14 indexed citations
3.
Wiest, Isabella C., F M Wolf, Dyke Ferber, et al.. (2025). Deidentifying Medical Documents with Local, Privacy-Preserving Large Language Models: The LLM-Anonymizer. NEJM AI. 2(4). 10 indexed citations
4.
Çifçi, Didem, Erwin Tomasich, Maximilian J. Mair, et al.. (2025). Density and entropy of immune cells within the tumor microenvironment of primary tumors and matched brain metastases. Acta Neuropathologica Communications. 13(1). 34–34. 1 indexed citations
5.
Çifçi, Didem, Gregory Patrick Veldhuizen, Wan-Jung Tsai, et al.. (2025). Deep learning predicts microsatellite instability status in colorectal carcinoma in an ethnically heterogeneous population in South Africa. Journal of Clinical Pathology. 79(1). 50–56.
6.
Wiest, Isabella C., et al.. (2025). How machine learning on real world clinical data improves adverse event recording for endoscopy. npj Digital Medicine. 8(1). 424–424.
7.
Nahhas, Omar S. M. El, Marko van Treeck, Georg Wölflein, et al.. (2024). From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology. Nature Protocols. 20(1). 293–316. 27 indexed citations
8.
Lipková, Jana & Jakob Nikolas Kather. (2024). The age of foundation models. Nature Reviews Clinical Oncology. 21(11). 769–770. 3 indexed citations
9.
Inojosa, Hernán, Isabel Voigt, Dyke Ferber, et al.. (2024). Integrating large language models in care, research, and education in multiple sclerosis management. Multiple Sclerosis Journal. 30(11-12). 1392–1401. 7 indexed citations
10.
11.
Dreyer, Tobias, Kai J. Borm, Ulrich A. Schatz, et al.. (2024). Expert-Guided Large Language Models for Clinical Decision Support in Precision Oncology. JCO Precision Oncology. 8(8). e2400478–e2400478. 16 indexed citations
12.
Khader, Firas, Gustav Müller‐Franzes, Soroosh Tayebi Arasteh, et al.. (2023). Denoising diffusion probabilistic models for 3D medical image generation. Scientific Reports. 13(1). 7303–7303. 121 indexed citations breakdown →
13.
Yuan, Tanwei, Dominic Edelmann, Jakob Nikolas Kather, et al.. (2023). CpG-biomarkers in tumor tissue and prediction models for the survival of colorectal cancer: A systematic review and external validation study. Critical Reviews in Oncology/Hematology. 193. 104199–104199. 2 indexed citations
14.
Khader, Firas, Gustav Müller‐Franzes, Tianyu Han, et al.. (2023). Multimodal Deep Learning for Integrating Chest Radiographs and Clinical Parameters: A Case for Transformers. Radiology. 309(1). e230806–e230806. 39 indexed citations
15.
Echle, Amelie, et al.. (2022). The future of artificial intelligence in digital pathology – results of a survey across stakeholder groups. Histopathology. 80(7). 1121–1127. 19 indexed citations
16.
Schmitt, Max, Roman C. Maron, Achim Hekler, et al.. (2021). Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study. Journal of Medical Internet Research. 23(2). e23436–e23436. 40 indexed citations
17.
Caldéraro, Julien & Jakob Nikolas Kather. (2020). Artificial intelligence-based pathology for gastrointestinal and hepatobiliary cancers. Gut. 70(6). 1183–1193. 69 indexed citations
18.
Kather, Jakob Nikolas, Johannes Krisam, Pornpimol Charoentong, et al.. (2019). Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Medicine. 16(1). e1002730–e1002730. 611 indexed citations breakdown →
19.
Kather, Jakob Nikolas, Pornpimol Charoentong, Meggy Suarez‐Carmona, et al.. (2018). High-Throughput Screening of Combinatorial Immunotherapies with Patient-Specific In Silico Models of Metastatic Colorectal Cancer. Cancer Research. 78(17). 5155–5163. 33 indexed citations
20.
Kather, Jakob Nikolas, Jan Poleszczuk, Meggy Suarez‐Carmona, et al.. (2017). In Silico Modeling of Immunotherapy and Stroma-Targeting Therapies in Human Colorectal Cancer. Cancer Research. 77(22). 6442–6452. 79 indexed citations

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.

Explore authors with similar magnitude of impact

Breakdown of academic impact, for papers by Justin Ko Breakdown of academic impact, for papers by Daniel L. Rubin Breakdown of academic impact, for papers by Alexander T. Pearson Breakdown of academic impact, for papers by Dong Ni Breakdown of academic impact, for papers by Ritse M. Mann Breakdown of academic impact, for papers by Frederick Klauschen Breakdown of academic impact, for papers by Nico Karssemeijer Breakdown of academic impact, for papers by Yun Liu Breakdown of academic impact, for papers by Hannah Gilmore Breakdown of academic impact, for papers by David Snead
Rankless by CCL
2026