Using artificial intelligence to support and streamline rapid systematic evidence reviews

Abstract

Abstract Issue The Rapid Evidence Service, initiated by the National Collaborating Centre for Methods and Tools (NCCMT) during COVID-19, supports public health decision making by conducting rapid reviews on priority topics. Description of issue Integral to rapid reviews is an expedited timeline but the quantity of available literature for most public health review questions takes significant time to screen manually. NCCMT integrated 4 artificial intelligence (AI) features into the screening process. DAISY Rank applies predictions learned from manual screening patterns to re-order remaining studies, with most relevant appearing first. AI Screening automatically screens remaining studies based on prediction scores. Check for Screening Errors and Re-Rank Report use previous screening patterns to identify studies that were potentially falsely excluded and predict the total number of included studies, respectively. These features were tested by comparing results provided by AI with those produced manually for select test sets. Results NCCMT used AI to support and expedite screening, assess screening progress, and/or minimize risk of inappropriately excluding studies for 35 rapid reviews on 20 topics. Using DAISY Rank enabled one screener to review over 4000 references in 9 hours, compared to a different review, where the same amount of screening took 28 hours without DAISY Rank. AI Screening correctly excluded up to 80% of irrelevant search results across reviews. Check for Screening Errors identified 37 potential includes manually excluded in one review; these were reviewed and 3 were included. Re-Rank Report allowed NCCMT to re-allocate staff to subsequent steps in the review process when most included studies were identified. Lessons Integrating AI features into screening led to less time required, better anticipated timelines, more accurate staff allocation and reduced errors. More rigorous study of AI best practices is needed to continue to improve rapid review method efficiencies. Key messages • Rapid reviews can be an important source of evidence for decision makers if they can be completed quickly but maintain rigor and accuracy. • AI holds promise as a way to improve screening efficiency.

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