ArticleYonsei medical journal2025
Comparative Analysis of Large Language Models for Answering Cancer-Related Questions in Korean.
Article in Yonsei medical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
1 citing paper in PubMed.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeLarge 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
methodsThe 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 ChatGPT and CLOVA X was calculated using KReaD, an artificial intelligence-based tool designed to objectively assess the complexity of Korean texts and reader comprehension.
resultsThe Wilcoxon test for the GQS score of answers using ChatGPT and CLOVA X showed that there is no statistically significant difference in quality between the two LLMs (
conclusionBoth ChatGPT and CLOVA X answered Korean-language cancer-related questions with no significant difference in overall quality.
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