在使用LLMs的LLM研究中评估准则的遵守
Ji Su Ko1, Hwon Heo2, Chong Hyun Suh3
1Department of Radiology, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Japanese journal of radiology
|February 5, 2026
概括
大型语言模型 (LLM) 在评估医学研究报告质量方面表现有前途,准确地提取明确的细节. 然而,他们与上下文依赖的信息扎,表明未来在科学分析中LLM发展的领域.
科学领域:
- 医学研究报告标准 医学研究报告标准
- 医疗保健中的人工智能
- 自然语言处理 (NLP) 应用程序
背景情况:
- MI-CLEAR-LLM检查清单旨在标准化涉及大型语言模型 (LLM) 的医学研究报告质量.
- 评估LLM对报告准则的遵守对于医学研究的透明度和可重复性至关重要.
- 之前的检查列表评估方法是手动的,耗时的.
研究的目的:
- 评估高级LLM,特别是GPT-4o和o1在自动评估遵守MI-CLEAR-LLM检查清单方面的能力.
- 在本评估任务中,比较基于文本的与基于图像的LLM模式的性能.
- 为了确定LLM驱动的检查列表评估的一致性和准确性.
主要方法:
- 分析了159篇专注于LLM应用的医学研究文章.
- 测试GPT-4o和o1模型在文本和图像模式中使用结构化提示与推理策略.
- 使用人类评估作为参考标准,并对每个模型进行三次独立试验以评估一致性.
主要成果:
- 无论是GPT-4o还是o1,在明确的LLM规范方面都取得了高准确度 (85.9-100%),在随机性参数方面也取得了良好的准确度 (63.6-95%).
- 对于诸如即时会话处理 (51.5-70.7%) 和测试数据独立性 (59.6-76.8%) 等上下文依赖项目的性能下降.
- 基于文本的模型显示出优异的试验间一致性 (GPT-4o-text: κ=0.926),而基于图像的模型显示出更大的变化 (κ=0.402-0.772).
结论:
- 在医学研究中,LLM具有显著的潜力,可以自动评估报告质量,特别是对结构化信息的评估.
- 在LLM的性能方面仍然存在挑战,即提取取上下文依赖或推断报告细节.
- 需要对LLM进行进一步的改进,以提高其批判性评估复杂研究报告元素的能力.
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