如何设计,创建和评估指令调整数据集用于医疗保健中的大型语言模型培训:从临床角度的教程
Wojciech Nazar1, Grzegorz Nazar2, Aleksandra Kamińska2
1Department of Allergology, Faculty of Medicine, Gdańsk Medical University, Gdansk, Poland.
Journal of medical Internet research
|March 18, 2025
概括
本教程指导医疗从业人员为医疗保健AI创建高质量的指令调整数据集 (ITD). 它涵盖数据采购,设计和评估,包括手动,人工智能生成和混合方法,用于医疗中强大的人工智能.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 数据科学数据科学数据科学
背景情况:
- 高质量的数据对于医疗保健决策和大型语言模型 (LLM) 的性能至关重要.
- 指令调整数据集 (ITD) 对于提高在各种任务中的LLM能力至关重要.
- 需要针对医疗保健应用程序创建ITD的可访问指导.
研究的目的:
- 提供关于设计,创建和评估医疗保健ITD的全面教程.
- 为医疗从业者使复杂的ITD概念易于理解.
- 在人工智能开发中弥合临床和技术领域之间的差距.
主要方法:
- 探索数据源和精心设计的数据集的特征.
- 检查三种主要数据集构建方法:手动,合成 (人工智能生成) 和混合.
- 讨论用于ITD质量的元数据选择和人类评估策略.
主要成果:
- 介绍了在医疗保健中建立ITD的结构化框架.
- 详细介绍了不同数据集准备方法的实际方法和考虑.
- 该教程强调了ITDs在推进医学人工智能的关键作用.
结论:
- 对于在医疗保健中开发有效的人工智能,ITD是必不可少的,即使是人工通用智能 (AGI) 的出现.
- 为了持续跨学科的进步,需要为ITDs建立一个统一的全球框架.
- 人类策划的ITD将仍然是人工智能处理和应用医学知识所必不可少的.
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