通过4个大型语言模型生成的项目对病理学持续认证的适用性进行比较
Gary W Procop1, Anthony Schlinsog1, Colette Earnest1
1The American Board of Pathology, Tampa, FL, United States.
American journal of clinical pathology
|February 21, 2026
概括
四个人工智能大语言模型 (LLM) 被评估为创建美国病理学会 (ABPath) 认证项目. 与Llama和Titan相比,Claude和ChatGPT的表现优越,这凸显了需要仔细选择LLM的需要.
科学领域:
- 医疗信息学 医疗信息学
- 教育中的人工智能
- 病理学持续认证的证书.
背景情况:
- 持续认证需要高质量的评估项目.
- 人工智能大语言模型 (LLM) 为项目生成提供了潜力.
- 根据既定标准对LLM绩效进行评估至关重要.
研究的目的:
- 评估四个LLM在生成符合美国病理学会 (ABPath) 标准的评估项目的能力.
- 为了比较不同LLMs在创建书面和实践考试项目中的表现.
主要方法:
- 一个信息学应用程序使用基于ABPath项目编写标准的提示.
- 主题专家 (中小企业) 评估了LLM生成的项目 (书面和实用) 的结构,准确性,相关性和难度.
- 中小企业对每个项目的LLM来源视而不见.
主要成果:
- 克劳德在书面 (86.1%) 和实践 (92.9%) 项目中获得了最高分.
- 然后是ChatGPT,在书面项目中得分为79.7%,在实践项目中得分为81.2%.
- 拉玛 (65.8%写作,65.8%实践) 和泰坦 (60.9%写作,56.8%实践) 的得分明显较低.
结论:
- 在生成符合ABPath的评估项目的LLM性能中存在显著差异.
- 用户必须评估特定的LLM,以确保它们满足任务要求.
- 假设LLM等效对于专业项目生成是不建议的.
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