About us: Research group “Clinical Artificial Intelligence

We are a young, diverse, and interdisciplinary group of scientists using 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 immunooncology. However, we routinely apply our methods to other disease areas as well, for example to transplantation medicine. To learn more about our research, have a look at our featured publications.

Our affiliations

Our lab is affiliated with the Faculty of Medicine and the Faculty of Computer Science at Technical University Dresden and is part of the Else Kroener Fresenius Center for Digital Health. In addition, our group is affilated with the Department of Medical Oncology at the National Center for Tumor Diseases Heidelberg, and the Department of Pathology and Data Analytics, University of Leeds, Leeds, UK.

Our funding

Our research is funded by the Else Kroener Fresenius Foundation, the German Cancer Aid (Deutsche Krebshilfe, “Max Eder research group”), the German Federal Ministry of Health (BMG, “DEEP LIVER”), German Federal Ministry of Education and Research (BMBF, “PEARL”) and the Innovation Fund of the “Gemeinsamer Bundesausschuss” (G-BA, “Transplant.KI”).


Planetary health is a prerequisite for individual health. For us as scientists and clinicians, it is imperative to spread awareness: we need to limit global temperature increase and restore biodiversity to protect health.

Recent Posts

Our lab is moving to Dresden

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Towards targeted treatment of cancer with Artificial Intelligence

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Survey on AI applications in pathology

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Our research featured in the news

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How to develop AI biomarkers for the clinic

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New job opportunity in our group

Our research featured in the news

Large international consortium for deep learning-based genotyping of colorectal cancer: MSIDETECT

New publication: Pan-cancer image-based detection of clinically actionable genetic alterations

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