Using large language models to automate summarization of CT simulation orders in radiation oncology
This study demonstrated the high precision and consistency of the LLaMa 3.1 405B model in extracting keywords and summarizing CT simulation orders, suggesting that LLMs have great potential to assist in this task, reduce the workload of CT simulation therapists and improve radiation oncology workflow efficiency.
Authors
- Louis Lam
- Meiyun Cao
- Samir H. Patel (ORCID: https://orcid.org/0000-0002-8251-574X)
- Jason Sharp
- Edward Clouser (ORCID: https://orcid.org/0009-0009-5900-9433)
- Jason Holmes (ORCID: https://orcid.org/0000-0001-8943-9471)
- Wei Liu (ORCID: https://orcid.org/0000-0003-0935-3999)
- P. Wang (ORCID: https://orcid.org/0000-0002-6730-1524)
- Sujay A. Vora
- Diego Santos Toesca
- Xiaoning Ding
- S.X. Hu (ORCID: https://orcid.org/0009-0000-9423-5131)
- Wendy S. Lindholm
- Brianne C Raulston
Institutions
- George Washington University (US)
- Corcoran College of Art and Design (US)
- Mayo Clinic Hospital (US)
Publication Details
- Journal
- Journal of Applied Clinical Medical Physics
- Published
- 2025-10-27
- DOI
- https://doi.org/10.1002/acm2.70310
- Citations
- 1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.65
Funders
- National Cancer Institute