PuSH - Publication Server of Helmholtz Zentrum München: Collaborative framework on responsible AI in LLM-driven CDSS for precision oncology leveraging real-world patient data.

Navigation

Home

Deutsch

Research

Advanced Search

Browse by ...

... Journal

... Publication Type

... Research Data

... Publication Year

Publication overview

Support & Contact

Contact persons

Help

Data protection

Mathes, S.* ; Ferber, D.* ; Dreyer, T.* ; Borm, K.J.* ; Modersohn, L.* ; Willem, T. ; Dirven, R.* ; Vibert, J.* ; Kreutzfeldt, S.* ; Perez-Lopez, R.* ; Prelaj, A.* ; Strand, F.* ; Baird, R.D.* ; Boeker, M.* ; Kather, J.N.* ; Tschochohei, M.* ; Lammert, J.*

Collaborative framework on responsible AI in LLM-driven CDSS for precision oncology leveraging real-world patient data.

npj Precis. Oncol. 10:15 (2025)

Publ. Version/Full Text

Postprint

Research data

DOI

PMC

	Open Access Gold

Abstract
Metrics
Extra information

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.

Altmetric

Additional Metrics?

[➜Log in]

Edit extra informations Login

Publication type Article: Journal article

Document type Scientific Article

Keywords Artificial-intelligence

ISSN (print) / ISBN 2397-768X

e-ISSN 2397-768X

Journal npj Precision Oncology

Quellenangaben Volume: 10, Issue: 1, Article Number: 15

Publisher Springer

Publishing Place Heidelberger Platz 3, Berlin, 14197, Germany

Reviewing status Peer reviewed

Institute(s) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)

Grants Projekt DEAL