Related Experiment Video
Updated: Aug 27, 2025
Drug Repurposing Hypothesis Generation Using the "RE:fine Drugs" System
Published on: December 11, 2016
In a pilot study, automated real-time systematic review updates were feasible, accurate, and work-saving
Iain J Marshall1, Thomas A Trikalinos2, Frank Soboczenski1
1School of Life Course and Population Sciences, King's College London, London, UK.
Objectives:
The aim of this study is to describe and pilot a novel method for continuously identifying newly published trials relevant to a systematic review, enabled by combining artificial intelligence (AI) with human expertise.
Study Design And Setting:
We used RobotReviewer LIVE to keep a review of COVID-19 vaccination trials updated from February to August 2021. We compared the papers identified by the system with those found by the conventional manual process by the review team.
Results:
The manual update searches (last search date July 2021) retrieved 135 abstracts, of which 31 were included after screening (23% precision, 100% recall). By the same date, the automated system retrieved 56 abstracts, of which 31 were included after manual screening (55% precision, 100% recall). Key limitations of the system include that it is limited to searches of PubMed/MEDLINE, and considers only randomized controlled trial reports. We aim to address these limitations in future. The system is available as open-source software for further piloting and evaluation.
Conclusion:
Our system identified all relevant studies, reduced manual screening work, and enabled rolling updates on publication of new primary research.
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