December 8, 2025npj Precision OncologyOpen Access

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

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Authors

JKJakob Nikolas KatherUniversity of Leeds MTMaximilian TschochoheiTechnical University of Munich

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Implication

Proposed framework enhances biomarker identification and therapy in precision oncology, indicating responsible AI's potential impact on patient outcomes.

Key Points

Framework enhances responsible AI integration, focusing on precision oncology outcomes and patient data.
Key principles for practice were developed by multidisciplinary experts, benefiting from collaboration with Cancer Core Europe.
The study emphasizes the role of large language models in structuring data, improving the identification of therapies and biomarkers.
Highlights the necessity of responsible AI practices, calling for ethical considerations in the use of real-world patient data.

Cite This Study

Kather et al. (2025) studied this question.

synapsesocial.com/papers/693624ba4fa91c937236c88f https://doi.org/10.1038/s41698-025-01180-5

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Implication

Proposed framework enhances biomarker identification and therapy in precision oncology, indicating responsible AI's potential impact on patient outcomes.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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