Role of large language models in the multidisciplinary decision-making process for patients with renal cell carcinoma: a pilot experience
We evaluated an AI chatbot's ability to suggest diagnostic and therapeutic pathways for renal cell carcinoma (RCC) in a multidisciplinary tumor board (MDT). A retrospective analysis of 103 cases (2023-2024) found 62.1% agreement with MDT decisions (κ = 0.44, p< 0.001). Concordance was highest in when follow-up imaging was suggested (p = 0.001), with disease status influencing agreement (p = 0.004). These results suggest AI could assist in RCC case assessments, warranting further research.
Authors
- Chiara Casolani (ORCID: https://orcid.org/0000-0002-7730-012X)
- Riccardo Negrelli (ORCID: https://orcid.org/0000-0001-6014-0775)
- Riccardo Giuseppe Bertolo (ORCID: https://orcid.org/0000-0003-0260-4601)
- A Borsato
- Alessandro Antonelli (ORCID: https://orcid.org/0000-0002-5211-6342)
- Emanuela Fantinel (ORCID: https://orcid.org/0000-0002-1929-4016)
- Matteo Brunelli (ORCID: https://orcid.org/0000-0002-3832-2676)
- Maria Angela Cerruto (ORCID: https://orcid.org/0000-0002-1793-2336)
- Lorenzo De Bon
- Sarah Malandra (ORCID: https://orcid.org/0000-0003-1352-4754)
- Alessandro Veccia (ORCID: https://orcid.org/0000-0001-5947-3206)
- Greta Pettenuzzo (ORCID: https://orcid.org/0009-0008-3256-4624)
- Giulia Volpi (ORCID: https://orcid.org/0000-0001-9686-8821)
- Andrea Zivi
- Emiliano Salah El Din Tantawy
- Filippo Caudana
Institutions
- University of Verona (IT)
- Azienda Ospedaliera Universitaria Integrata Verona (IT)
Publication Details
- Journal
- npj Precision Oncology
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1038/s41698-025-01014-4
- Citations
- 5
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.27