关于放射学大语言模型研究的报告准则:国际德尔菲专家调查
Jonathan Kottlors1, Andra-Iza Iuga1, Christian Bluethgen2
1Institute of Diagnostic and Interventional Radiology, Faculty of Medicine and University Hospital Cologne, Kerpener Str 62, 50937, Cologne, Germany.
Radiology
|February 3, 2026
概括
为了提高研究质量,制定了放射学大语言模型 (LLM) 的报告准则. FLAIR检查清单标准化了报告,以提高可重复性和临床使用.
科学领域:
- 放射学 放射学是一门学科.
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 大型语言模型 (LLM) 显示了放射学在像图像分析和决策支持等任务中的巨大潜力.
- 然而,在LLM研究中缺乏标准化的报告,这阻碍了其可复制性和临床应用.
研究的目的:
- 为放射学领域的大型语言模型研究制定标准化报告准则.
- 提高LLM研究领域的透明度,可复制性和临床适用性.
主要方法:
- 进行了对57项放射学LLM研究的系统审查 (2023年5月至2024年3月).
- 采用了包括系统审查和20名国际专家在内的德尔菲过程在内的两步过程.
- 在6个关键类别中创建了一个32项检查清单,FLAIR (放射学LLM评估框架).
主要成果:
- FLAIR检查清单包括32个项目,涵盖数据输入,提示,性能,伦理和实施.
- 关键类别包括一般信息,提示/微调,绩效指标,伦理/透明度以及实施/风险.
- 该清单旨在在放射学领域的各种LLM应用中标准化报告.
结论:
- FLAIR检查清单提供了一个标准化的框架,用于报告放射学LLM研究.
- 标准化报告将促进透明度,可复制性和可比性,促进临床翻译.
- 采用FLAIR可以通过更可靠的AI驱动的放射学工具来增强患者护理.
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