一项研究协议,以全面评估两个基于人工智能的工具,以进行标题和抽象查,以制定基于证据的癌症指南
Xiaomei Yao1,2, Ashirbani Saha2, Sharan Saravanan3
1Department of Health Research Methods, Evidence, and Impact McMaster University Hamilton Ontario Canada.
Cancer innovation
|July 1, 2025
概括
本研究评估了人工智能 (AI) 工具DistillerSR和EPPI-Reviewer,用于癌症临床实践指南 (CPG) 开发中的标题和摘要查. 目标是与人类审核员相比,评估它们的效率和准确性,从而有可能加快CPG创建速度.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
- 在瘤学瘤学.
背景情况:
- 系统性审查 (SRs) 对于临床实践指南 (CPG) 的制定至关重要,但需要大量的时间.
- 像DistillerSR和EPPI-Reviewer这样的人工智能 (AI) 工具在加快SR标题和摘要选阶段显示出有希望.
- 这些AI工具在不同癌症SRs中的通用性和一致性需要进一步调查.
研究的目的:
- 评估和比较DistillerSR和EPPI-Reviewer在癌症CPG发展的标题和抽象查 (I阶段) 中与人类审查员的表现.
- 评估这些人工智能工具在SR过程中提供的工作量和时间节省.
- 通过检查错过文章的百分比来确定人工智能辅助选的准确性和全面性.
主要方法:
- 将采用一个追溯模拟研究设计.
- 该研究将分析PEBC (基于证据的护理计划) 之前发布的10个CPG,涵盖五种高发病率癌症类型.
- 每个人工智能工具将进行30次模拟试验,每次为1个CPG,主要结果集中在工作量和时间节约上,次要结果集中在错过的文章上.
主要成果:
- 本节介绍了研究方案;实际结果正在等待验证.
- 根据期刊标准,将以表格形式提供说明性数据.
- 最终研究中的所有数据都将经过彻底的验证.
结论:
- 本研究协议概述了第一个比较DistillerSR和EPPI-Reviewer在癌症CPGSR中进行I期查的研究.
- 结果将指导人工智能工具在未来癌症指南开发中的可靠整合.
- 建议进行前性研究,以证实这种回顾性分析的结果.
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