大型语言模型解决欧洲放射学文凭:比较评估
Hamza Eren Güzel1, Laura Oleaga2, Ali Murat Koç3
1Department of Radiology, Ministry of Health Izmir City Hospital, İzmir, Turkey (H.E.G.).
Academic radiology
|February 14, 2026
概括
两个先进的AI模型,GPT-5和Gemini 2.5 Pro,通过了欧洲放射学文凭 (EDiR) 考试,显示了放射学教育的潜力. 然而,在全面临床部署之前,视觉和时间理解方面仍然存在挑战.
科学领域:
- 医疗成像中的人工智能
- 放射学 教育 技术 技术
- 大型语言模型 (LLM)
背景情况:
- 多模式大语言模型 (LLM) 正在快速发展,在放射学教育和评估方面显示出巨大潜力.
- 在标准化医疗检查中评估这些LLM的表现对于了解他们的能力和局限性至关重要.
研究的目的:
- 为了比较三个最先进的LLM的性能:GPT-5,Gemini 2.5 Pro和Claude 4.5 Sonnet.
- 通过退休的欧洲放射学文凭 (EDiR) 考试来评估他们的能力.
主要方法:
- 包括多重响应问题,简短案例和CORE案例在内的EDiR检查是在零射击条件下对LLM进行的.
- 输入内容包括文本,静态图像和视频;回复根据欧洲放射学委员会官方标准得分.
- 通过值是基于历史的人类表现数据.
主要成果:
- 双子 2.5 Pro (72.6%) 和 GPT-5 (67.3%) 超过了书面组件的合格率 (50.9%),而克劳德 4.5 索内特得分为47.5%.
- GPT-5 (62.5%) 和Gemini 2.5 Pro (56.3%) 通过了CORE组件 (55%的通过率),而Claude 4.5 Sonnet (50.7%) 没有通过.
- 模型在文本和静态图像任务中表现出色,但在特定坐标定位和动态视频解释方面遇到了困难.
结论:
- GPT-5和Gemini 2.5 Pro符合EDiR通过标准,表明它们适合扩大放射学教育.
- 尽管语义处理取得了进展,但空间定位和时间推理的局限性仍然存在,阻碍了自主临床部署.
- 临床视觉接地仍然是目前放射学LLM的一个关键挑战.
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