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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14110%2F25%3A00143837" target="_blank" >RIV/00216224:14110/25:00143837 - isvavai.cz</a>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 ' 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 ' 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30204 - Oncology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

CLINICAL AND TRANSLATIONAL RADIATION ONCOLOGY

ISSN

2405-6308

e-ISSN

2405-6308

Svazek periodika

51

Číslo periodika v rámci svazku

March 2025

Stát vydavatele periodika

IE - Irsko

Počet stran výsledku

6

Strana od-do

1-6

Kód UT WoS článku

001412579300001

EID výsledku v databázi Scopus

2-s2.0-85214317706