Comparative Analysis of Large Language Models for Answering Cancer-Related Questions in Korean.

ABSTRACT:

Purpose

Large language models (LLMs) have shown potential in medicine, transforming patient education, clinical decision support, and medical research. However, the effectiveness of LLMs in providing accurate medical information, particularly in non-English languages, remains underexplored. This study aimed to compare the quality of responses generated by ChatGPT and Naver's CLOVA X to cancer-related questions posed in Korean.

Materials and methods

The study involved selecting cancer-related questions from the National Cancer Institute and Korean National Cancer Information Center websites. Responses were generated using ChatGPT and CLOVA X, and three oncologists assessed their quality using the Global Quality Score (GQS). The readability of the responses generated by ChatGP

SUBMITTER: Chang H 

PROVIDER: S-EPMC12206594 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature