A Study Protocol for a Comprehensive Evaluation of Two Artificial Intelligence-Based Tools in Title and Abstract Screening for the Development of Evidence-Based Cancer Guidelines

IF 2
Xiaomei Yao, Ashirbani Saha, Sharan Saravanan, Ashley Low, Jonathan Sussman
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Abstract

Background

Conducting a systematic review (SR) is a time-intensive process and represents the first phase in developing a clinical practice guideline (CPG). Completing a CPG through the Program in Evidence-Based Care (PEBC), a globally acknowledged guideline program supported by Ontario Health (Cancer Care Ontario), typically takes about 2 years. Thus, expediting an SR can significantly reduce the overall time required to complete a CPG. Our recently published review identified two artificial intelligence (AI) tools, DistillerSR and EPPI-Reviewer that reduced time in the title and abstract screening in an SR process when developing a CPG. However, the consistency and generalizability of these tools remain unclear within or across different SRs related to cancer. This study protocol aims to evaluate and compare the performance of DistillerSR and EPPI-Reviewer against human reviewers for title and abstract screening (Stage I screening) in cancer CPG development.

Methods

We will conduct a retrospective simulation study to evaluate and compare the performance of DistillerSR and EPPI-Reviewer across 10 previously published CPGs by PEBC. These CPGs include the five cancer types with the highest incidence (lung, breast, prostate, colorectal, and bladder). We will run 30 simulation trials for one CPG per AI tool. Primary outcomes are workload savings and time savings in Stage I screening. The secondary outcome is the percentage of missing articles among the final included articles. This informs the accuracy and comprehensiveness of the AI tools. Descriptive and inferential statistical analysis will be conducted to evaluate the outcomes.

Results

This is a study protocol. The data presented in the tables are illustrative examples rather than actual study results, in accordance with the journal s standard structure. All data included in the final study will be thoroughly validated.

Discussion

This will be the first study to investigate and compare the performance of DistillerSR and EPPI-Reviewer in Stage I screening of SRs in CPGs across different cancer types. These findings will inform the reliable use of AI tools in future cancer-related CPGs. The results from this retrospective study will need to be confirmed by prospective studies.

Abstract Image

在标题和摘要筛选中对两种基于人工智能的工具进行综合评估的研究方案,以制定循证癌症指南
背景:进行系统评价(SR)是一个耗时的过程,是制定临床实践指南(CPG)的第一阶段。通过循证护理计划(PEBC)完成CPG,这是由安大略省健康(安大略省癌症护理)支持的全球公认的指导计划,通常需要2年左右的时间。因此,加速SR可以显著减少完成CPG所需的总时间。我们最近发表的综述确定了两种人工智能(AI)工具,DistillerSR和EPPI-Reviewer,它们在开发CPG时减少了SR过程中标题和摘要筛选的时间。然而,这些工具在与癌症相关的不同SRs内部或之间的一致性和普遍性仍不清楚。本研究方案旨在评估和比较DistillerSR和EPPI-Reviewer与人类审稿人在癌症CPG发展中的标题和摘要筛选(I期筛选)的表现。我们将进行回顾性模拟研究,以评估和比较PEBC先前发表的10个cpg中的DistillerSR和EPPI-Reviewer的性能。这些CPGs包括五种发病率最高的癌症类型(肺癌、乳腺癌、前列腺癌、结肠直肠癌和膀胱癌)。我们将为每个AI工具的一个CPG运行30次模拟试验。主要结果是在I期筛查中节省工作量和时间。次要结果是最终纳入文章中缺失文章的百分比。这说明了人工智能工具的准确性和全面性。将进行描述性和推断性统计分析来评估结果。这是一项研究方案。根据该杂志的标准结构,表格中的数据是说明性示例,而不是实际研究结果。最终研究中包含的所有数据都将被彻底验证。这将是第一个调查和比较DistillerSR和EPPI-Reviewer在不同癌症类型的CPGs中筛选SRs的I期表现的研究。这些发现将为未来癌症相关CPGs中人工智能工具的可靠使用提供信息。这项回顾性研究的结果需要通过前瞻性研究来证实。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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