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

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F25%3A00080134" target="_blank" >RIV/00209805:_____/25:00080134 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S2405630825000047" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405630825000047</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ctro.2025.100914" target="_blank" >10.1016/j.ctro.2025.100914</a>

Result language

angličtina

Original language name

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

Original language description

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, with fine-tuning based on pediatric radiotherapy guides from various institutions. Additionally, a radiation oncologist prepared answers to these questions. Finally, a multi-institutional group of nine pediatric radiotherapy experts conducted a blind review of responses, assessing reliability, concision, and comprehensibility. Results: The radiation oncologist and GPT-4 provided the highest-quality responses, though GPT-4&apos;s answers were often excessively verbose. While fine-tuned GPT-3.5 generally outperformed basic GPT-3.5, it often provided overly simplistic answers. Inadequate responses were rare, occurring in 4% of GPT-generated responses across all models, primarily due to GPT-3.5 generating excessively long responses. Conclusions: LLMs can be valuable tools for educating patients and their families before treatment in pediatric radiation oncology. Among them, only GPT-4 provides information of a quality comparable to that of a radiation oncologist, although it still occasionally generates poor-quality responses. GPT-3.5 models should be used cautiously, as they are more likely to produce inadequate answers to patient questions.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

30204 - Oncology

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Clinical and Translational Radiation Oncology

ISSN

—

e-ISSN

2405-6308

Volume of the periodical

51

Issue of the periodical within the volume

March 2025

Country of publishing house

IE - IRELAND

Number of pages

6

Pages from-to

100914

UT code for WoS article

001412579300001

EID of the result in the Scopus database

2-s2.0-85214317706