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A Comparative Study of Five Large Language Models' Response for Liver Cancer Comprehensive Treatment.

Deyuan Zhong1, Yuxin Liang1, Hong-Tao Yan1

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Summary

Large language models (LLMs) show varied performance in liver cancer queries. GPT-4 and Kimi offer promise, but limitations in complex reasoning require domain-specific optimization before clinical use.

Keywords:
ChatGPTclinical applicabilitylarge language modelsliver cancermedical chatbot

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Area of Science:

  • Artificial Intelligence in Medicine
  • Oncology
  • Hepatobiliary Diseases

Background:

  • Large language models (LLMs) are increasingly adopted in healthcare.
  • Their reliability in specialized clinical domains like liver cancer is uncertain.
  • Liver cancer presents unique challenges for AI applications due to its complexity.

Purpose of the Study:

  • To evaluate the comprehensibility and clinical applicability of five mainstream LLMs.
  • To assess LLM performance on liver cancer-related clinical questions.
  • To identify strengths and weaknesses of current LLMs in oncology.

Main Methods:

  • Developed 90 standardized liver cancer management questions.
  • Blindly evaluated five LLMs (GPT-4, Gemini, Copilot, Kimi, Ernie Bot) by hepatobiliary experts.
  • Scored responses for comprehensibility and clinical applicability using predefined criteria.

Main Results:

  • Kimi (68%) and GPT-4 (62%) showed the highest fully applicable responses.
  • Comprehensibility was high (Kimi, Ernie Bot >98%), but guideline concordance was inconsistent.
  • LLMs struggled with complex, professional-level questions compared to common-sense ones.

Conclusions:

  • LLMs exhibit variable performance in liver cancer clinical queries.
  • GPT-4 and Kimi demonstrate potential for clinical applicability.
  • Domain-specific optimization is crucial for integrating LLMs into liver cancer care, especially for complex decisions.