Published November 20, 2025
| Version 1.1
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Commentary on the ESMO Guidance for LLM Use in Oncology: Narrative and Process Divergences
Authors/Creators
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1.
University of Milan
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2.
Fondazione IRCCS Istituto Nazionale dei Tumori
Description
This preprint analyzes the ESMO guidance on large language models (LLMs) in clinical oncology and examines discrepancies between the Delphi statements and the narrative section of the document. The commentary highlights issues related to scope interpretation, narrative overextension, and the limitations of stateless LLM systems within clinical decision-making frameworks.
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Additional details
Related works
- Cites
- Peer review: 10.1016/j.annonc.2025.09.001 (DOI)
References
- Wong EYT, Verlingue L, Aldea M, et al. ESMO guidance on the use of large language models in clinical practice (ELCAP). Ann Oncol. 2025. doi:10.1016/j.annonc.2025.09.001.
- Lekadir K, Frangi AF, Porras AR, Glocker B, Cintas C, Langlotz CP, et al. FUTURE-AI: International consensus guideline for trustworthy and deployable artificial intelligence in healthcare. BMJ. 2025;388:e081554. doi:10.1136/bmj-2024-081554.
- Natangelo S. The narrative continuity test (NCT): A framework for assessing state, identity and continuity in large language models. arXiv [Preprint]. 2025. doi:10.48550/arXiv.2510.24831
- European Union. Regulation (EU) 2024/1689 of the European Parliament and of the Council of 13 June 2024 laying down harmonised rules on artificial intelligence (AI Act). Off J Eur Union. 2024.