用大型语言模型在语言生成AI中评估医学物理知识:使用医学物理学家考试
Noriyuki Kadoya1, Kazuhiro Arai2, Shohei Tanaka2
1Department of Radiation Oncology, Tohoku University Graduate School of Medicine, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan. kadoya.n@rad.med.tohoku.ac.jp.
Radiological physics and technology
|September 10, 2024
概括
在回答日本医学物理考试问题方面,ChatGPT-4显著超过了ChatGPT-3.5. 然而,即使是先进的AI模型在辐射计量学等专业领域也存在局限性.
科学领域:
- 医学物理 医学物理
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 大型语言模型 (LLM) 在专业领域显示出潜力.
- 在专业认证考试中评估AI表现对于理解能力至关重要.
研究的目的:
- 评估ChatGPT-3.5和ChatGPT-4.0在日本医学物理学家委员会考试中的表现.
- 在这些人工智能模型中建立医学物理知识的基准.
主要方法:
- 从2018-2022年日本医学物理学家委员会考试中使用了考试问题.
- 将来自ChatGPT-3.5和ChatGPT-4.0的AI生成答案与正确答案进行比较.
- 分析了不同医学物理类别的准确率.
主要成果:
- 聊天GPT-4的平均准确率为72.7%±2.6%,明显高于聊天GPT-3.5的42.2%±2.5%.
- 对于ChatGPT-4,准确度在大多数类别中超过60%,但在辐射计量学 (55.6%) 和与辐射相关的法律/伦理学 (40.0%) 中仍低于60%.
结论:
- 与ChatGPT-3.5.5相比,ChatGPT-4在医学物理知识方面表现优越,与ChatGPT-3.5.5相比.
- 目前的LLM需要进一步开发,以便在所有专业的医学物理领域中获得高精度.
- 这些发现为开发人工智能驱动的医学物理工具提供了基础数据,例如日本输入辐射治疗支持系统.
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