Fabiano Nicholas, Gupta Arnav, Bhambra Nishaant, Luu Brandon, Wong Stanley, Maaz Muhammad, Fiedorowicz Jess G, Smith Andrew L, Solmi Marco
Department of Psychiatry University of Ottawa Ottawa Ontario Canada.
Department of Medicine University of Calgary Calgary Alberta Canada.
JCPP Adv. 2024 Apr 23;4(2):e12234. doi: 10.1002/jcv2.12234. eCollection 2024 Jun.
Systematic reviews are a cornerstone for synthesizing the available evidence on a given topic. They simultaneously allow for gaps in the literature to be identified and provide direction for future research. However, due to the ever-increasing volume and complexity of the available literature, traditional methods for conducting systematic reviews are less efficient and more time-consuming. Numerous artificial intelligence (AI) tools are being released with the potential to optimize efficiency in academic writing and assist with various stages of the systematic review process including developing and refining search strategies, screening titles and abstracts for inclusion or exclusion criteria, extracting essential data from studies and summarizing findings. Therefore, in this article we provide an overview of the currently available tools and how they can be incorporated into the systematic review process to improve efficiency and quality of research synthesis. We emphasize that authors must report all AI tools that have been used at each stage to ensure replicability as part of reporting in methods.
系统评价是综合给定主题现有证据的基石。它们同时有助于识别文献中的空白，并为未来研究提供方向。然而，由于现有文献的数量不断增加且复杂性日益提高，进行系统评价的传统方法效率较低且耗时更长。众多人工智能（AI）工具不断推出，有望优化学术写作效率，并在系统评价过程的各个阶段提供协助，包括制定和完善检索策略、根据纳入或排除标准筛选标题和摘要、从研究中提取关键数据以及总结研究结果。因此，在本文中，我们概述了当前可用的工具，以及如何将它们纳入系统评价过程以提高研究综合的效率和质量。我们强调，作者必须报告在每个阶段使用的所有人工智能工具，以确保可重复性，作为方法报告的一部分。
