大型语言模型在放射学实习室的表现 培训中的考试问题
1Izmir Katip Celebi University, Ataturk Training and Research Hospital, Department of Radiology, Izmir, Turkey (A.S.).
Academic radiology
|November 12, 2025
概括
先进的AI语言模型在放射学考试中表现出强的表现,新版本提高了准确性. 虽然它们对教育有帮助,但在图像解释和特定子专业方面仍然面临挑战.
科学领域:
- 医疗教育中的人工智能
- 放射学培训和评估 放射学培训和评估
背景情况:
- 大型语言模型 (LLM) 越来越多地被探索在放射学教育中的潜力.
- 评估高级LLM在标准化放射学评估上的表现至关重要.
研究的目的:
- 评估几个领先的LLM在放射学住院培训中的考试问题上的准确性.
- 为了比较较新的LLM版本与旧模型和人类居民的性能.
主要方法:
- 从机构放射学考试 (2023-2025) 中分析了282个多选择题 (仅以文本和图像为基础).
- 测试了五个LLM:ChatGPT-4o,ChatGPT-5,克劳德4 Opus,克劳德4.1 Opus,以及双子座2.5 Pro.
- 将LLM的准确率与放射科住院医生的准确率进行比较;对幻觉的评估.
主要成果:
- 双子 2.5 Pro实现了最高的整体准确度 (83.0%),紧随其后的是ChatGPT-5 (82.3%),表现优于居民 (78.2%).
- 像ChatGPT-5和Gemini 2.5 Pro这样的新型号显示出与旧版本相比的显著准确性改进.
- 居民在以图像为基础的问题 (80.4%) 中表现出色,而LLM在仅以文本为基础的问题中领先;没有发现幻觉.
结论:
- 在放射学评估中,LLM表现良好,最近的版本有显著的改进.
- 基于图像的解释和某些子专业存在局限性,表明需要仔细验证.
- 最好将LLM视为放射学教育中的辅助工具,需要持续改进和数据验证.
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