Using large language models to automate summarization of CT simulation orders in radiation oncology.

ABSTRACT:

Background

In the current clinical workflow of radiation oncology, therapists manually summarize the physician-issued Computed Tomography (CT) simulation orders to prepare patients' CT simulations. This process increases the workload, introduces variability in documentation quality, and is prone to human errors.

Purpose

This study aims to address these challenges using a large language model (LLM) to automate the generation of summaries from the CT simulation orders and evaluate its performance.

Methods

A total of 607 CT simulation orders were collected from the Aria database at our institution. A locally hosted LLaMa 3.1 405B model, accessed via the Application Programming Interface (API) service, was used to extract keywords from the CT simulation orders and generat

SUBMITTER: Cao M 

PROVIDER: S-EPMC12558592 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature