Clinical Artificial Intelligence

Deep learning, foundation models, and agents in biomedical research and healthcare.

We are an interdisciplinary research team of computer scientists, medical doctors, biologists, engineers, and everything in between. Our expertise lies in the deep learning-based analysis of complex clinical data, ranging from routine histology and radiology images to genomic sequences, text, and multimodal datasets.

In the last few years, we have pioneered the field of AI-based biomarkers in precision oncology. We routinely train and utilize transformer-based foundation models to build complex workflows for diagnosis, prognosis, and predictive modeling.

Our research also includes the development of autonomous agents for science and healthcare. As AI evolves from narrow, single-task tools into generalist systems, we are developing agents capable of reasoning over data and orchestrating clinical tasks. Our mission is to bridge the gap between these rapidly evolving computational capabilities and the needs of clinical routine.

From 1 October 2026, Kather Lab will be part of the Institute for Medical AI (IMAI) at Heidelberg University Hospital. Our team spans Heidelberg and Dresden, with continuing links to TU Dresden and the Else Kröner Fresenius Center for Digital Health. The lab also continues its work in Medical Oncology at Heidelberg University Hospital and NCT Heidelberg.

Kather Lab group photo, summer 2025
Group photo, summer 2025.

Research Focus

Computational Pathology

Mapping cancer biomarkers to phenotypes observed in histopathology images. Predicting genetic alterations and treatment responses directly from histology slides using foundation models.

Agents & Foundation Models

Moving beyond narrow AI tools towards autonomous agents that can reason over data, orchestrate complex workflows, and support clinical decision-making.

Natural Language Processing

Leveraging Large Language Models (LLMs) to extract information from unstructured clinical data and assist in guideline-based decision making.

Swarm Learning & Privacy

Decentralized AI techniques that keep patient data local and secure while training robust models across international networks.

Selected Publications

A selection of 12 key publications. For a full list, please visit Google Scholar.

Towards autonomous medical artificial intelligence agents. Ferber D, ..., Kather JN. Nature, 2026. [View]
Safety and security of large language models in healthcare. Clusmann J, ..., Kather JN. Nature, 2026. [View]
ESMO Basic Requirements for AI-based Biomarkers In Oncology (EBAI). Aldea M, ..., Kather JN. Annals of Oncology, 2025. [View]
ESMO guidance on the use of Large Language Models in Clinical Practice (ELCAP). Wong EYT, ..., Kather JN. Annals of Oncology, 2025. [View]
Development and validation of an autonomous artificial intelligence agent for clinical decision-making in oncology. Ferber D, El Nahhas OSM, ..., Kather JN. Nature Cancer, 2025. [View]
Multimodal histopathologic models stratify hormone receptor-positive early breast cancer. Boehm KM, ..., Kather JN. Nature Communications, 2025. [View]
From whole-slide image to biomarker prediction: end-to-end weakly supervised deep learning in computational pathology. El Nahhas OSM, ..., Kather JN. Nature Protocols, 2024. [View]
GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines. Ferber D, ..., Kather JN. NEJM AI, 2024. [View]
Deep learning-based phenotyping reclassifies combined hepatocellular-cholangiocarcinoma. Calderaro J, ..., Kather JN. Nature Communications, 2023. [View]
Swarm learning for decentralized artificial intelligence in cancer histopathology. Saldanha OL, ..., Kather JN. Nature Medicine, 2022. [View]
Transformer-based biomarker prediction from colorectal cancer histology: A large-scale multicentric study. Wagner SJ, ..., Kather JN. Cancer Cell, 2023. [View]
Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Kather JN, et al. Nature Medicine, 2019. [View]

Developed Tools

Open Source Commitment

We are strong believers in open and reproducible science. Most of our projects are available as open-source code on GitHub. We invite you to explore our repositories, use our models, and contribute to our work.

Visit our GitHub

Principal Investigator

Prof. Jakob N. Kather

Prof. Dr. med. Jakob N. Kather, MSc

Hopp Foundation Professor of Artificial Intelligence in Medicine
Founding Director, IMAI
From 1 October 2026

  • Heidelberg University · from 1 October 2026
  • IMAI, Heidelberg University Hospital · from 1 October 2026
  • Continuing affiliation: TU Dresden

On 1 October 2026, Professor Jakob Nikolas Kather will take up the Hopp Foundation Professorship for “Artificial Intelligence in Medicine” at the Faculty of Medicine at Heidelberg University. He will also become founding director of the Institute for Medical AI (IMAI) at Heidelberg University Hospital and remain affiliated with TU Dresden. He is a physician-scientist, board-certified in internal medicine, with a master’s degree in Medical Physics. Since 2022, he has held the Chair of Clinical Artificial Intelligence at TU Dresden.

His work focuses on using Deep Learning to extract actionable information from routine clinical data including histopathology, radiology, and clinical text. He is a strong advocate for an interdisciplinary approach where medical researchers learn to code and computer scientists immerse themselves in cancer biology.

He is passionate about building the next generation of foundation models, agentic AI, and multimodal AI systems and translating them into clinical practice. He is leading European initiatives to establish guidelines for AI in oncology, including ESMO EBAI and ELCAP.

Selected Roles

  • From 1 October 2026: Hopp Foundation Professor, Heidelberg University; Founding Director, IMAI
  • Dresden: Professor of Clinical Artificial Intelligence since 2022; continuing affiliation with TU Dresden from October 2026
  • Since 2024: Program Committee Member, AACR Annual Meeting 2026 Program Committee, ESMO 2025 Annual Meeting, ESMO AI 2025-2027
  • Since 2024: Editor, Annals of Oncology, ESMO RWDDDO, npj Precision Oncology, AACR Cancer Research Communications

Awards & Honors

  • 2025: Felix Burda Award (Medicine & Science)
  • 2022: Thannhauser Award (DGVS)
  • 2021: Heinz Maier-Leibnitz Prize (DFG)
  • 2021: German Cancer Prevention Award

Major Funding (Selection)

We are grateful to our funders and supporters, including

  • ERC Starting Grant "NADIR"
  • EU Horizon "ODELIA" & "GENIAL"
  • German Cancer Aid "DECADE"
  • BMFTR "DECIPHER-M", "TANGERINE", "TransformLiver"
  • BCRF "BELLADONNA"

Our Team

Our team spans Heidelberg and Dresden, connecting expertise in clinical artificial intelligence, oncology, and computational medicine.

Join Us

We are always looking for new team members, interns, visiting researchers, visiting professors, entrepreneurs in residence, and academic collaborators.

If you are passionate about AI, Oncology, and solving real clinical problems, we want to hear from you.

Apply Now Contact us via this form.