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生物化学教育中的大型语言模型:绩效的比较评估

Olena Bolgova¹, Inna Shypilova², Volodymyr Mavrych¹

¹College of Medicine, Alfaisal University, Al Takhassousi St, Riyadh, 11533, Saudi Arabia.

JMIR medical education

|April 10, 2025

概括

医学中的大型语言模型 (LLM) 与学生相比,在医学生物化学问题上表现优越. 克劳德AI实现了最高的准确性,表明AIAI.

科学领域:

人工智能在医学中的应用
医疗教育技术技术医学教育
大型语言模型 (LLM)

背景情况:

人工智能和法学士的进步正在彻底改变医学.
在通过医学委员会考试方面,LLM显示出潜力.
需要验证LLM能够回答特定学科的医疗问题的能力.

研究的目的:

为了比较先进的LLM聊天机器人的表现与医学生化学医学学生的表现.
分析Claude,GPT-4,Gemini和Copilot在生物化学多选题中的准确性.

主要方法:

利用了200个美国医学执照考试 (USMLE) 风格的多选择题 (MCQ),涵盖23个主题.
评价了克劳德3.5索内特,GPT-4-1106,双子座1.5闪光和副驾驶员的5次尝试,以获得准确性.
用于统计分析的千平方测试 (P<.05).

主要成果:

聊天机器人平均准确率为81.1%,比学生高出8.3% (P=.02).
克劳德实现了最高的准确性 (92.5%),其次是GPT-4 (85%),双子座 (78.5%) 和副驾驶员 (64%).
表现最好的主题包括eicosanoids,生物能量学,赫索索单酸盐通路和体.

结论:

人工智能模型在特定的医学领域表现出明显的优势,可能有助于生物化学教育.
士学位的表现表明AI在医学教育和评估中的巨大潜力.
人工智能工具可以为医学专业学科的学生提供有针对性的支持.

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