在医学文献中挖掘人类-人工智能合作的基础模型
Zifeng Wang1, Lang Cao1, Qiao Jin2
1School of Computing and Data Science, University of Illinois Urbana-Champaign, Urbana, IL, USA.
ArXiv
|July 30, 2025
概括
一个新的AI基础模型,LEADS,改善了医学文献挖掘以证据为基础的医学. 它增强了研究选择和数据提取,提高了专家在临床试验分析中的效率和准确性.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 基于证据的医学基于证据的医学.
背景情况:
- 系统的文献审查对基于证据的医学至关重要.
- 当前的人工智能模型在医疗文献挖掘中面临各种任务和治疗领域的局限性.
研究的目的:
- 介绍LEADS,一个用于医学文献研究搜索,查和数据提取的AI基础模型.
- 评估LEADS的表现与通用大型语言模型 (LLM) 相比,以及其对专家工作流程的影响.
主要方法:
- 在633,759个指令数据点 (LEADSInstruct) 上,从系统性审查,临床试验出版物和注册表中进行LEADS培训.
- 在六个医学文献挖掘任务中对LEADS与四个通用LLM进行比较分析.
- 一项涉及16名临床医生和研究人员的用户研究,评估LEADS对研究选择和数据提取效率和准确性的影响.
主要成果:
- 在六个任务上,LEADS表现出与四个通用LLM相比的持续改进.
- 在研究选择中使用LEADS的专家实现了更高的回忆率 (0.81比0.77),节省了22.6%的时间.
- 在数据提取方面,使用LEADS的专家实现了更高的准确性 (0.85比0.80),节省了26.9%的时间.
结论:
- 像LEADS这样的专业医学文献基础模型可以超过通用LLMs.
- 在医学文献挖掘中,LEADS显著提高了专家工作流程的质量和效率.
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