Precision Oncology in Non-small Cell Lung Cancer: A Comparative Study of Contextualized ChatGPT Models
The performance gains observed following contextualization suggest that broader applications of LLMs in oncology may exist than current literature indicates. This study provides proof of concept for the use of contextualized GPT models in oncology and showcases their accessibility. Future studies validating this application within additional cancer types or real-life patient encounters could provide an important bridge to eventual adoption.
Authors
- Morana Vojnic (ORCID: https://orcid.org/0000-0001-5441-4119)
- Harshal A. Shah (ORCID: https://orcid.org/0000-0003-4088-6232)
- Max Ward (ORCID: https://orcid.org/0000-0001-9114-7339)
- Ethan Brown (ORCID: https://orcid.org/0009-0003-6933-0745)
- Brianna M. Donnelly (ORCID: https://orcid.org/0000-0002-4109-0589)
- Randy S. D’Amico (ORCID: https://orcid.org/0000-0002-3047-945X)
Institutions
- Donald & Barbara Zucker School of Medicine at Hofstra/Northwell (US)
Publication Details
- Journal
- Cureus
- Published
- 2025-03-24
- DOI
- https://doi.org/10.7759/cureus.81097
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72