Accelerating Biomedical Research in Oncology

We harness the power of generative and agentic AI to transform biomedical research and data-driven innovation. Our AI solutions help researchers, pharmaceutical companies, and healthcare organizations extract valuable insights from complex datasets, accelerating scientific discovery and driving the rapid adoption of groundbreaking treatments.

Scientific Evidence

Our AI solutions are based on scientific evidence. Here you find selected publications of our team.

Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology

Nature Cancer · 2025

Ferber D, El Nahhas OSM, Wölflein G, Wiest IC, Clusmann J, Leßmann M-E, Foersch S, Lammert J, Tschochohei M, Jäger D, Salto-Tellez M, Schultz N, Truhn D, Höper C, Kather JN.

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LLM Agents Making Agent Tools

ACL · 2025

Wölflein G, Ferber D, Truhn D, Arandjelović O, Kather JN.

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GPT-4 for Information Retrieval and Comparison of Medical Oncology Guidelines

NEJM AI · 2024

Ferber D, Wiest IC, Wölflein G, Ebert MP, Beutel G, Eckardt J-N., Truhn D, Springfeld C, Jäger D, Kather JN.

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End-To-End Clinical Trial Matching with Large Language Models

ArXiv · 2024

Ferber D, Hilgers L, Wiest IC, Leßmann M-E, Clusmann J, Neidlinger P, Zhu J, Wölflein G, Lammert J, Tschochohei M, Böhme H, Jäger D, Aldea M, Truhn D, Höper C, Kather JN.

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In-context learning enables multimodal large language models to classify cancer pathology images

Nature Communications · 2024

Ferber D, Wölflein G, Wiest IC, Ligero M, Sainath S, Ghaffari Laleh N, El Nahhas OSM, Müller-Franzes G, Jäger D, Truhn D, Kather JN.

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Large language models should be used as scientific reasoning engines, not knowledge databases

Nature Medicine · 2023

Truhn D, Reis-Filho JS, Kather JN.

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By integrating cutting-edge AI with deep domain expertise, we empower researchers and industry partners to harness the full potential of biomedical data.