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在耳鼻喉科委员会考试中利用先进的大型语言模型:使用python和应用程序编程接口进行调查.
Cosima C Hoch1, Paul F Funk2, Orlando Guntinas-Lichius2
1Department of Otolaryngology, Head and Neck Surgery, TUM School of Medicine and Health, Technical University of Munich (TUM), Ismaningerstrasse 22, 81675, Munich, Germany. cosima.chiara.hoch@tum.de.
概括
先进的AI语言模型对耳鼻喉科考试有希望,但GPT-3.5 Turbo对耳鼻喉科考试有希望.
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
- 耳鼻喉科 诊断 诊断 耳鼻喉科 诊断
背景情况:
- 大型语言模型 (LLM) 越来越多地被用于专门的医疗应用.
- 在董事会认证考试中评估LLM绩效对于理解其实用性至关重要.
- 之前对GPT-3.5 Turbo的评估为纵向性能跟踪提供了基线.
研究的目的:
- 评估高级LLM (GPT-4变体,双子座,克劳德) 在耳鼻喉科委员会考试问题上的能力.
- 为了评估GPT-3.5 Turbo在一年中的纵向性能变化.
- 为了比较不同LLM系列在专业医疗领域的准确性.
主要方法:
- 使用了2576个耳鼻喉科委员会认证问题的问题库.
- 测试了11个LLM,包括GPT-3.5 Turbo,GPT-4变种,Gemini和Claude.
- 通过API管理问题,使用Python脚本进行数据收集.
主要成果:
- GPT-4o获得了最高的准确性,特别是在过敏学和头部瘤中.
- 克劳德模型具有竞争力,但通常表现不佳的GPT-4变体.
- 与前一年相比,GPT-3.5 Turbo的准确性显著下降.
- 在所有模型中,单选题的准确性高于多选题.
结论:
- 新的LLM在专业医疗内容和认证方面显示出巨大的潜力.
- 在GPT-3.5 Turbo的性能下降凸显了持续LLM评估的必要性.
- 持续优化和高效的API使用对于在医学教育中推进LLM至关重要.
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