Related Experiment Video
Updated: Jan 9, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
Published on: June 13, 2025
Collaborative framework on responsible AI in LLM-driven CDSS for precision oncology leveraging real-world patient
Sonja Mathes1,2, Dyke Ferber3,4,5, Tobias Dreyer6,7
1TUM School of Medicine and Health, Department of Dermatology, Technical University of Munich, Munich, Germany. sonja.mathes@mri.tum.de.
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.
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