Large Language Models as Decision-Making Tools in Oncology: Comparing Artificial Intelligence Suggestions and Expert Recommendations
LLMs, particularly Claude3-Opus and GPT4-Turbo, demonstrated promising accuracy in suggesting appropriate adjuvant treatments for patients with early BC on the basis of their medical records. Although LLMs showed limitations in validating surgery and indicating genomic tests, their performance in other treatment modalities highlights their potential to automate and augment decision making during MDTs. Further studies with fine-tuned LLMs and a prospective design are needed to demonstrate their utility in clinical practice.
Authors
- Victoire Brillaud-Meflah
- Magali Le Blanc‐Onfroy
- Franciszek Tomaszewski (ORCID: https://orcid.org/0000-0003-1774-7437)
- Jean‐Sébastien Frenel (ORCID: https://orcid.org/0000-0002-5273-0561)
- Stéphane Supiot (ORCID: https://orcid.org/0000-0003-2094-1708)
- Caroline Rousseau (ORCID: https://orcid.org/0000-0002-0455-0523)
- Tanguy Perennec (ORCID: https://orcid.org/0000-0002-1118-2271)
- Loic Ah-Thiane (ORCID: https://orcid.org/0000-0003-4978-1912)
- Mario Campone (ORCID: https://orcid.org/0000-0002-5196-5908)
- A. Mervoyer
- Pierre-Étienne Heudel (ORCID: https://orcid.org/0000-0002-2155-549X)
- Marie Robert (ORCID: https://orcid.org/0000-0002-1374-6210)
Institutions
- Centre Léon Bérard (FR)
- Institut Génétique Nantes Atlantique (FR)
Publication Details
- Journal
- JCO Clinical Cancer Informatics
- Published
- 2025-03-01
- DOI
- https://doi.org/10.1200/cci-24-00230
- Citations
- 8
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 10.88