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

Hyun Chang1, Jin-Woo Jung2, Yongho Kim3

  • 1Department of Medical Oncology and Hematology, International St. Mary's Hospital, Catholic Kwandong University, Incheon, Korea. hchang@ish.ac.kr.

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|June 24, 2025
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This study compared ChatGPT and CLOVA X for Korean cancer questions. Both large language models (LLMs) provided similar quality answers, with CLOVA X showing slightly better readability.

Keywords:
Korean languageLarge language modelcancerpatients

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Oncology

Background:

  • Large language models (LLMs) show promise in healthcare applications like patient education and clinical decision support.
  • The efficacy of LLMs in delivering accurate medical information, especially in non-English languages, requires further investigation.

Purpose of the Study:

  • To compare the quality of responses from ChatGPT and Naver's CLOVA X for Korean cancer-related queries.
  • To evaluate the readability of responses generated by these two leading LLMs in Korean.

Main Methods:

  • Cancer-related questions were sourced from reputable cancer information websites.
  • Responses from ChatGPT and CLOVA X were evaluated by three oncologists using the Global Quality Score (GQS).
  • Readability was assessed using KReaD, an AI tool for Korean text complexity.

Main Results:

  • No statistically significant difference was found in the overall quality (GQS) of responses between ChatGPT and CLOVA X (p>0.05).
  • CLOVA X achieved higher readability scores (KReaD) compared to ChatGPT (p=0.036), although perceived ease of reading showed no significant difference.
  • Both models demonstrated comparable performance in terms of 'Good' vs. 'Poor' ratings.

Conclusions:

  • ChatGPT and CLOVA X offer comparable overall quality for answering Korean cancer-related questions.
  • While CLOVA X may be slightly more readable according to AI assessment, both LLMs are viable tools for providing cancer information in Korean.