Large language models for pretreatment education in pediatric radiation oncology: A comparative evaluation study.

ABSTRACT:

Background and purpose

Pediatric radiotherapy patients and their parents are usually aware of their need for radiotherapy early on, but they meet with a radiation oncologist later in their treatment. Consequently, they search for information online, often encountering unreliable sources. Large language models (LLMs) have the potential to serve as an educational pretreatment tool, providing reliable answers to their questions. We aimed to evaluate the responses provided by generative pre-trained transformers (GPT), the most popular subgroup of LLMs, to questions about pediatric radiation oncology.

Materials and methods

We collected pretreatment questions regarding radiotherapy from patients and parents. Responses were generated using GPT-3.5, GPT-4, and fine-tuned GPT-3.5, wi

SUBMITTER: Wawrzuta D 

PROVIDER: S-EPMC11762905 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature