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Benchmarking Large Language Models for Cervical Spondylosis.

Boyan Zhang1,2, Yueqi Du1,2, Wanru Duan1,2

  • 1Xuanwu Hospital, Capital Medical University, Beijing, China.

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|August 5, 2024
PubMed
Summary
This summary is machine-generated.

Large language models (LLMs) can help cervical spondylosis patients access medical advice. GPT-4 demonstrated the highest accuracy among tested LLMs in answering patient questions about this common spinal disorder.

Keywords:
ChatGPTLLMcervical spondylosislarge language modelpatient

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Area of Science:

  • Spinal Disorders
  • Medical Informatics
  • Artificial Intelligence in Healthcare

Background:

  • Cervical spondylosis is a prevalent degenerative spinal condition.
  • Patients need accessible medical information for managing their condition.
  • Large language models (LLMs) present a new avenue for patient medical advice.

Purpose of the Study:

  • To evaluate the accuracy of LLM-generated answers for common cervical spondylosis patient questions.
  • To compare the performance of different LLMs in providing medical information for this condition.

Main Methods:

  • Collected frequently asked questions from cervical spondylosis patients.
  • Utilized multiple large language models to generate answers.
  • Assessed the accuracy of LLM responses by three experienced spinal surgeons.

Main Results:

  • All evaluated LLMs provided satisfactory answers to patient queries.
  • GPT-4 exhibited the highest accuracy rate in its responses.
  • Analysis revealed varying capabilities and areas for improvement across different LLMs.

Conclusions:

  • LLMs are a viable tool for providing medical information to cervical spondylosis patients.
  • GPT-4 shows superior performance in accuracy for this specific application.
  • Further AI development is needed to enhance LLM capabilities in medical advice.