Mitchell Evan, Are Elisha B, Colijn Caroline, Earn David J D
Department of Mathematics and Statistics, McMaster University, Hamilton, ON,Canada.
Department of Mathematics, Simon Fraser University, Burnaby, BC,Canada.
PLoS One. 2025 Apr 3;20(4):e0320151. doi: 10.1371/journal.pone.0320151. eCollection 2025.
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.
实时证据综合（LES）涉及定期反复更新系统评价或荟萃分析，以便将新证据纳入总结结果。在文献检索、收集和数据提取阶段，这需要投入大量人力时间。现有工具可实现相关期刊文章检索的自动化，但目前从这些文章中提取数据仍是人工操作。在本文中，我们展示了一个概念验证的Python程序，该程序利用人工智能（AI）工具（具体而言，ChatGPT）解析一批期刊文章并提取相关结果，在不影响准确性的前提下，大幅减少了此操作中的人力时间投入。我们的程序在一组估算新冠病毒病平均潜伏期的期刊文章上进行了测试，平均潜伏期是数学建模中一个重要的流行病学参数。我们还讨论了与信息总量以及信息发送到AI引擎的速率相关的重要局限性。这项工作有助于推动关于AI应用以及此类工具在科学研究中所能发挥作用的持续讨论。
