Sonja Mathes, Dyke Ferber, Tobias Dreyer, Kai J. Borm, Luise Modersohn, Theresa Willem, Richard Dirven, Julien Vibert, Simon Kreutzfeldt, Raquel Pérez-López, Arsela Prelaj, Fredrik Strand, Richard D. Baird, Martin Boeker, Jakob Nikolas Kather, Maximilian Tschochohei, Jacqueline Lammert
Precision oncology leverages real-world data, essential for identifying biomarkers and therapies. Large language models (LLMs) can aid at structuring unstructured data, overcoming current bottlenecks in precision oncology. We propose a framework for responsible LLM integration into precision oncology, co-developed by multidisciplinary experts and supported by Cancer Core Europe. Five thematic dimensions and ten principles for practice are outlined and illustrated through application to uterine carcinosarcoma in a thought experiment.