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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月4日

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

摘要

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.