Using artificial intelligence tools to automate data extraction for living evidence syntheses

Evan Mitchell, Elisha B Are, Caroline Colijn and David J D Earn

PLOS ONE, 2025, vol. 20, issue 4, 1-8

Abstract: Living evidence synthesis (LES) involves repeatedly updating a systematic review or meta-analysis at regular intervals to incorporate new evidence into the summary results. It requires a considerable amount of human time investment in the article search, collection, and data extraction phases. Tools exist to automate the retrieval of relevant journal articles, but pulling data out of those articles is currently still a manual process. In this article, we present a proof-of-concept Python program that leverages artificial intelligence (AI) tools (specifically, ChatGPT) to parse a batch of journal articles and extract relevant results, greatly reducing the human time investment in this action without compromising on accuracy. Our program is tested on a set of journal articles that estimate the mean incubation period for COVID-19, an epidemiological parameter of importance for mathematical modelling. We also discuss important limitations related to the total amount of information and rate at which that information can be sent to the AI engine. This work contributes to the ongoing discussion about the use of AI and the role such tools can have in scientific research.

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0320151

DOI: 10.1371/journal.pone.0320151

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