快速:可靠和高效 提交的自动生成 报告 检查列表与大型语言模型对接
Zeming Li1, Xufei Luo2, Zhenhua Yang3
1Department of Computer Science, Hong Kong Baptist University, Hong Kong SAR 999077, China.
概括
使用大型语言模型的RAPID工具,自动生成医疗报告准则的检查列表,提高作者和编辑的效率. 它在CONSORT和CONSORT-AI数据集上显示了高准确性和一致性.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 科学出版科学出版
背景情况:
- 在医学研究中,准确和全面的报告对于可重现性和临床决策至关重要.
- 现有的手动检查列表生成流程可能耗时且容易出现错误.
- 人工智能 (AI) 的整合为提高报告质量和效率提供了潜在的解决方案.
研究的目的:
- 评估RAPID的性能,这是一个自动报告检查列表生成工具.
- 评估该工具在利用大型语言模型 (LLM) 和检索增强生成 (RAG) 进行医疗报告方面的有效性.
主要方法:
- 使用LLM开发RAG架构,以创建RAPID工具.
- 根据CONSORT和CONSORT-AI指南,收集并手动注释了91项已发表的随机对照试验 (RCT).
- 数据集包括50个没有AI的RCT和41个使用AI干预的RCT.
主要成果:
- 快速结合了所有37个CONSORT报告项目,达到92.11%的平均准确率和81.14%的内容一致性.
- 对于CONSORT-AI,RAPID包括11个AI特定项目,达到83.81%的平均准确率和72.51%的内容一致性.
- 与其他方法相比,该工具在两个数据集上都展示了最先进的性能.
结论:
- 快速有效地自动生成检查列表,节省时间和提高医疗作者,研究人员,编辑和审稿人的效率.
- 该工具表现出强大的可扩展性和适应性各种医疗报告指南,无需广泛的再培训.
- 快速代表了人工智能辅助的医疗报告质量保证的重大进展.
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