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

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.

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Publication Details

Journal
npj Precision Oncology
Published
2025-12-04
DOI
https://doi.org/10.1038/s41698-025-01180-5
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

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

Raquel Pérez-López, Maximilian Tschochohei, Kai Joachim Borm, Luise Modersohn et al.
npj Precision Oncology
Artificial Intelligence in Healthcare and Education
article

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

Raquel Pérez-López, Maximilian Tschochohei, Kai Joachim Borm, Luise Modersohn, Jacqueline Lammert, Martin Boeker, Dyke Ferber, Simon Kreutzfeldt, Fredrik Strand, Sonja Mathes, Jakob Nikolas Kather, Theresa Willem, Richard J. Dirven, Richard D. Baird, Tobias F. Dreyer, Arsela Prelaj, Julien Vibert
article en

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.

npj Precision OncologyVol. 10(1)
Karolinska University Hospital (SE), Radboud University Nijmegen (NL), German Cancer Research Center (DE), Johannes Gutenberg University Mainz (DE), Heidelberg University (DE), Institut Gustave Roussy (FR), University Hospital Heidelberg (DE), Radboud University Medical Center (NL), Karolinska Institutet (SE), The Netherlands Cancer Institute (NL), Helmholtz Zentrum München (DE), Cancer Research UK Cambridge Center (GB), University Medical Center of the Johannes Gutenberg University Mainz (DE), Fresenius (Germany) (DE), National Center for Tumor Diseases (DE), Fondazione IRCCS Istituto Nazionale dei Tumori (IT), Vall d'Hebron Institute of Oncology (ES), Technical University of Munich (DE)
Openalex Percentile: Top 68%
Artificial Intelligence in Healthcare and Education
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