Journal Article

DKFZ-2025-02745

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png Collaborative framework on responsible AI in LLM-driven CDSS for precision oncology leveraging real-world patient data.

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

2026
Springer Nature [London]

Abstract: 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.

Note: 10, Article number: 15 (2026)

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Contributing Institute(s):

1. DKTK Koordinierungsstelle München (MU01)
2. Translationale Medizinische Onkologie (B340)

Research Program(s):

1. 312 - Funktionelle und strukturelle Genomforschung (POF4-312) (POF4-312)

Appears in the scientific report 2025

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Database coverage:
; ; ; Article Processing Charges ; BIOSIS Previews ; Biological Abstracts ; Clarivate Analytics Master Journal List ; Current Contents - Life Sciences ; DOAJ Seal ; Essential Science Indicators ; Fees ; IF >= 5 ; JCR ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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