医学研究中的基础和大语言模型报告检查清单 (REFINE):国际共识指南
Ismail Mese1, Tugba Akinci D'Antonoli2,3, Christian Bluethgen4,5
1Uskudar State Hospital, Department of Radiology, Istanbul, Türkiye.
新的报告准则REFINE确保了基础模型 (FM) 和医学大语言模型 (LLM) 的透明和可重复的研究. 这份检查清单有助于医学和成像AI研究.
科学领域:
- 医学的人工智能 (AI)
- 基金会模型 (FM) 是一个基础模型.
- 大型语言模型 (LLM)
背景情况:
- 基础模型 (FM) 和大语言模型 (LLM) 在医学研究中越来越多地使用.
- 缺乏标准化的报告准则阻碍了FM/LLM研究的透明度和可重复性.
- 需要一个全面的框架来报告涉及文本,图像和结构化数据的医学AI研究.
研究的目的:
- 为创立和大型laNguagE模型 (REFINE) 开发REporting检查清单.
- 为医学研究中的FM和LLM研究建立透明和可复制的国际报告准则.
- 包括用于成像AI应用程序的报告标准.
主要方法:
- 与国际专家一起使用修改后的Delphi流程来建立报告标准.
- 协议开发,专家招聘和德尔菲轮由一个指导委员会协调.
- 基于结构化评级和反的代修订,随后是术语和说明的协调阶段.
主要成果:
- 来自17个国家的57位贡献者和来自16个国家的54位小组成员参与了REFINE的开发.
- 一个由44项组成的六部分框架,标准化术语和详细的报告说明.
- 有一个在线平台可用于实际实施REFINE检查清单.
结论:
- REFINE为医学FM和LLM研究提供基于共识的报告标准,包括成像AI.
- 该指南促进了透明,可比,可重复的报告.
- 在医学和成像AI研究中促进可靠的证据合成.
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