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Study Protocol | Open Access

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

Xiaomei Yao1,2 ( ), Ashirbani Saha2 ( ), Sharan Saravanan3, Ashley Low3, Jonathan Sussman2
Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
Department of Oncology, McMaster University, Hamilton, Ontario, Canada
Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada

The first two authors contributed equally to this article.

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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.

Graphical Abstract

References

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Cancer Innovation
Article number: e70021

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Cite this article:
Yao X, Saha A, Saravanan S, et al. 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. Cancer Innovation, 2025, 4(4): e70021. https://doi.org/10.1002/cai2.70021

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Received: 29 November 2024
Revised: 19 May 2025
Accepted: 27 May 2025
Published: 29 June 2025
© 2025 The Author(s). Tsinghua University Press.

This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.