Clinical Artificial Intelligence
We are a young, diverse, and interdisciplinary group of scientists. We use computational methods to extract actionable knowledge from clinical routine data. Our main tools are Artificial Intelligence and Computational Modeling. We combine these tools with a clinical perspective on health and disease. Our main area of expertise is precision oncology of solid tumors, including immunotherapy. We are global thought leaders in the area of predicting clinically actionable properties of tumors directly from routinely available histopathology slides. Our lab’s mission is to build an interdisciplinary space in which young biologists, medical doctors and computer scientists collaborate and co-develop ideas and methods for improved clinical decision making in cancer.
Lab members
find overview https://jnkather.github.io/team/
Selected publications
Check out Google Scholar or PubMed for a full list of publications. Below some recent publications are highlighted:
Saldanha OL, Quirke P, West NP, … Kather JN. Swarm learning for decentralized artificial intelligence in cancer histopathology. Nature Medicine (2022), doi: 10.1038/s41591-022-01768-5. Epub ahead of print.
Kather JN, Pearson AT, Halama N, … Luedde T. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer, Nature Medicine (2019), doi: 10.1038/s41591-019-0462-y
Kather JN, Heij LR, Grabsch HI, … Luedde T. Pan-cancer image-based detection of clinically actionable genetic alterations, Nature Cancer (2020), doi: 10.1038/s43018-020-0087-6
Echle A, Grabsch HI, Quirke P, … Kather JN. Clinical-Grade Detection of Microsatellite Instability in Colorectal Tumors by Deep Learning, Gastroenterology (2020), doi: 10.1053/j.gastro.2020.06.021
Kather JN, Calderaro J. Development of AI-based pathology biomarkers in gastrointestinal and liver cancer, Nature Reviews Gastroenterology and Hepatology (2020), doi: 10.1038/s41575-020-0343-3
Contact
© Anja Stübner
Clinical Artificial Intelligence