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视觉语言模型在日本核医学委员会考试中的表现:文本准确度高,但对图像解释有挑战
Rintaro Ito1,2, Keita Kato3, Marina Higashi3
1Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan. ito.rintaro.k5@f.mail.nagoya-u.ac.jp.
Annals of nuclear medicine
|July 15, 2025
概括
视觉语言模型 (VLMs) 在核医学考试中表现有前途,在基于文本的问题上表现出色,但在图像分析方面扎. 虽然它们是有用的助手,但它们不能取代专家的口译或全面的培训.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 核医学教育教育 核医学教育
背景情况:
- 视觉语言模型 (VLMs) 集成视觉和文本数据,快速推进AI能力.
- 最先进的VLM的有效性,特别是在核医学等专业领域,仍然被低估.
- 根据既定的医学知识基准来评估VLM的性能对于了解其潜力和局限性至关重要.
研究的目的:
- 评估领先视觉语言模型 (VLMs) 在核医学委员会考试问题上的表现.
- 确定当前VLM在处理基于文本和基于图像的医疗问题方面的优缺点.
- 将VLM的能力与日本核医学委员会 (JNMBE) 考试要求进行比较.
主要方法:
- 利用了来自JNMBE (2022-2024) 的180个多选题,大约三分之一包含诊断图像.
- 测试了八种先进的VLM,包括ChatGPT变种,克劳德3.7,双子座2.0闪光,拉玛3.2和杰玛3.
- 通过多数投票确定准确性,每种模型经过三次确定性运行后,根据专家提供的参考答案进行验证.
主要成果:
- 总体准确度在36.1%至83.3%之间,ChatGPT o1 pro获得了最高的得分 (83.3%).
- 所有的VLM在纯文本问题上表现明显好 (例如,ChatGPT o1 pro的89.5%),而不是基于图像的问题 (例如,ChatGPT o1 pro的66.0%).
- 在最近的考试年份 (2022-2024) 中,VLMs在日本法规问题上表现出局限性,准确性略有下降.
结论:
- VLM在基于文本的核医学问题上表现出很高的准确性,但在图像解释方面存在明显的局限性.
- 目前的VLM可以作为基于文本的医疗查询的宝贵助手,但不能取代全面的培训或专家临床判断.
- 快速发展的VLM和考试难度的年度变化需要持续的评估和绩效指标的上下文解释.
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