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.

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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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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Autonomous Artificial Intelligence Agents for Clinical Decision Making in Oncology.

ArXiv · 2024

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