Study protocol for evaluating automation of systematic review processes with EPPI-Reviewer and Copilot 365 in updating the cataract evidence gap map

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Abstract Background The process of developing and updating an evidence gap map (EGM) is based on the principles of systematic reviews and requires extensive time and financial resources. Artificial intelligence (AI) tools, like prioritisation screening (PS), integrated into programmes such as EPPI-Reviewer (ER) and Copilot 365, can potentially mimic human performance in systematic review processes. ER is a subscription-based web application employed by systematic review groups, while Copilot 365, integrated into Microsoft 365, offers real-time assistance. Although ER shows promise in speeding up screening, the optimal threshold for accuracy remains unclear. Additionally, there is no evidence on the effectiveness of any version of Copilot in systematic review and EGM processes. Objectives Assess the accuracy and efficiency of Copilot 365 and PS integrated into ER at different stages of an EGM update, comparing it to human performance. Methods We will conduct both manual and automated screening of references, full-text screening, data extraction, and critical appraisal. Two reviewers will independently screen studies for inclusion, extract data, and appraise included studies, resolving conflicts through discussion. We will assess the accuracy and efficiency of Copilot 365 and ER at different EGM update stages, comparing them to human performance. To evaluate the PS accuracy, we will use 20% and 40% manual screening thresholds, calculating the proportion of relevant references prioritised by PS and the total relevant citations missed. We will compare Copilot 365’s full-text screening accuracy to reviewers’ decisions and assess consistency using Cohen’s Kappa. For automated data extraction and appraisal, we will manually inspect 20% of Copilot 365’s outputs, comparing them to reviewers’ results, measuring consistency with Cohen’s Kappa, and evaluating time savings by comparing the time taken for manual extraction versus using Copilot 365. Discussion This study will offer insights into ER’s accuracy in screening small samples of citations and potentially guide future applications in this context. Additionally, by evaluating Copilot 365, which shares similar features with other AI tools, we will gain a broader understanding of its applicability and limitations in evidence synthesis, making the results relevant to other AI applications in this field. Systematic review registration Registered at Open Science Framework: https://doi.org/10.17605/OSF.IO/49BX8 .

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