Department of Pharmacotherapy, College of Pharmacy, University of Utah, Utah, USA.
Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand.
Res Synth Methods. 2022 May;13(3):353-362. doi: 10.1002/jrsm.1553. Epub 2022 Feb 28.
The exponential increase in published articles makes a thorough and expedient review of literature increasingly challenging. This review delineated automated tools and platforms that employ artificial intelligence (AI) approaches and evaluated the reported benefits and challenges in using such methods. A search was conducted in 4 databases (Medline, Embase, CDSR, and Epistemonikos) up to April 2021 for systematic reviews and other related reviews implementing AI methods. To be included, the review must use any form of AI method, including machine learning, deep learning, neural network, or any other applications used to enable the full or semi-autonomous performance of one or more stages in the development of evidence synthesis. Twelve reviews were included, using nine different tools to implement 15 different AI methods. Eleven methods were used in the screening stages of the review (73%). The rest were divided: two in data extraction (13%) and two in risk of bias assessment (13%). The ambiguous benefits of the data extractions, combined with the reported advantages from 10 reviews, indicating that AI platforms have taken hold with varying success in evidence synthesis. However, the results are qualified by the reliance on the self-reporting of the review authors. Extensive human validation still appears required at this stage in implementing AI methods, though further evaluation is required to define the overall contribution of such platforms in enhancing efficiency and quality in evidence synthesis.
发表的文章数量呈指数级增长，使得对文献进行全面而及时的回顾变得越来越具有挑战性。本综述描述了使用人工智能 (AI) 方法的自动化工具和平台，并评估了使用这些方法的报告益处和挑战。截至 2021 年 4 月，在 4 个数据库（Medline、Embase、CDSR 和 Epistemonikos）中进行了系统评价和其他相关评价的检索，这些评价采用了 AI 方法。要被包括在内，该综述必须使用任何形式的 AI 方法，包括机器学习、深度学习、神经网络或任何其他用于实现证据综合开发的一个或多个阶段的完全或半自动执行的应用程序。共纳入 12 项综述，使用 9 种不同的工具实施了 15 种不同的 AI 方法。11 种方法用于综述的筛选阶段（73%）。其余的方法分别是：2 种用于数据提取（13%）和 2 种用于偏倚风险评估（13%）。数据提取的益处存在不确定性，再加上 10 项综述报告的优势，表明 AI 平台在证据综合方面取得了不同程度的成功。然而，由于综述作者的自我报告，结果存在一定的局限性。在实施 AI 方法的现阶段，似乎仍然需要进行广泛的人工验证，尽管需要进一步评估来确定这些平台在提高证据综合的效率和质量方面的总体贡献。
