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Clinical application potential of large language model: a study based on thyroid nodules.

Shujun Xia1,2, Qing Hua1, Zihan Mei1

  • 1Department of Ultrasound, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

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|July 30, 2024
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Summary

Large language models (LLMs) like ChatGPT-3.5 and New Bing Chat show potential in thyroid nodule diagnosis but do not match doctor accuracy. Further development is needed for clinical decision-making capabilities.

Keywords:
Artificial intelligenceChatGPTLLMNew Bing Chat

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Area of Science:

  • Artificial Intelligence in Medicine
  • Medical Informatics
  • Natural Language Processing

Background:

  • Limited data exists on large language models (LLMs) functioning as medical doctors.
  • Investigated the performance of ChatGPT-3.5 and New Bing Chat in a clinical context.

Purpose of the Study:

  • To evaluate the accuracy and reproducibility of ChatGPT-3.5 and New Bing Chat responses.
  • To compare LLM performance against junior and senior doctors using thyroid nodules as a model.

Main Methods:

  • 145 thyroid nodule patients' questions were used to query ChatGPT-3.5 and New Bing Chat five times each.
  • LLM responses were compared to answers from five junior doctors and five senior doctors (gold standard).
  • Evaluated accuracy and reproducibility of LLM-generated answers.

Main Results:

  • ChatGPT-3.5 and New Bing Chat had lower accuracy than junior doctors for specific questions (Q2, Q3, Q5).
  • Both LLMs showed comparable performance to junior doctors for other questions (Q4, Q6).
  • New Bing Chat (72.41%) demonstrated higher accuracy than ChatGPT-3.5 (58.62%) in decision-making, but both lagged behind junior doctors (89.66%).

Conclusions:

  • LLMs show promise for medical applications in thyroid nodule diagnosis and management.
  • Current LLMs do not possess the clinical decision-making capacity of human doctors.
  • Further research and development are necessary to bridge the gap between LLM capabilities and clinical practice.