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评估ChatGPT 3.5和4.0在StatPearls眼镜整形外科的性能基于文本和图像的考试问题

Gurnoor S Gill¹, Jacob Blair², Steven Litinsky³

¹Medical School, Florida Atlantic University Charles E. Schmidt College of Medicine, Boca Raton, USA.

|December 18, 2024

概括

与以前的版本相比,ChatGPT-4.0在眼膜问题上表现出更好的准确性. 然而,它在以图像为基础的问题上的表现落后于仅以文本为基础的问题,凸显了在医学教育中需要进一步的LLM进步的需要.

科学领域:

人工智能在医学中的应用
眼科眼科子专业眼科子专业
大型语言模型 (LLM)

背景情况:

大型语言模型 (LLM) 显示出作为医疗辅助工具的潜力.
之前的研究评估了眼科子专业的LLM绩效.
有限的研究存在于基于图像的医学问题LLM能力.

研究的目的:

评估ChatGPT版本3.5和4.0的眼膜问题.
为了比较纯文本和基于图像的问卷格式的表现.
使用StatPearls和OphthoQuestions问题库来评估LLM的准确性.

主要方法:

利用了 StatPearls 的 343 个纯文本和 127 个基于图像的眼镜问题.
包括来自OphthoQuestions的89个眼球塑形问题.
比较了ChatGPT-3.5和ChatGPT-4.0.0的正确性,答案分布和快速需要.

主要成果:

在纯文本问题上,ChatGPT-4.0实现了73.46%的准确性,明显超过ChatGPT-3.5 (56.85%).
在基于图像的眼膜问题上,ChatGPT-4.0的准确性为56.94%,明显低于其仅以文本的性能.
两种ChatGPT版本都显示出与人类表现的中等相关性,而ChatGPT-4.0的相关性略高.

结论:

与以前的版本相比,ChatGPT-4.0在眼膜学子专业中表现更好.
准确性挑战仍然存在,特别是基于图像的提示,需要在医疗应用中谨慎使用.
进一步的LLM发展对于可靠地融入医学教育和实践至关重要.

关键词:

人工智能聊天gpt-4-4的人工智能大型语言模型 (llm)医学检查医学检查医学检查眼球塑料是一种眼球塑料.眼膜造形轨道外科手术是眼膜造形的

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