在系统审查过程中,数据提取是一项耗时且资源密集的任务。自然语言处理(NLP)人工智能(AI)技术具有自动化数据提取的潜力,节省时间和资源,加快审查进程,提高提取数据的质量和可靠性。在本文中,我们提出了一种方法,使用BingAI和MicrosoftEdge作为第二个审阅者来验证和增强由单个人工审阅者首先提取的数据项。我们描述了指导BingAIChat工具将研究特征作为数据项从PDF文档中提取到表格中所涉及的步骤的工作示例,以便可以将它们与手动提取的数据进行比较。我们表明,这种技术可以为可用资源有限或新手审阅者的数据提取提供额外的验证过程。然而,在没有进一步评估和验证的情况下,它不应被视为已经建立和验证的双重独立数据提取方法的替代品。在系统评价中使用人工智能技术进行数据提取应在报告中透明和准确地描述。未来的研究应该集中在准确性上,效率,完整性,以及使用BingAI进行数据提取的用户体验,与独立使用两个或多个审阅者的传统方法相比。
Data extraction is a time-consuming and resource-intensive task in the systematic
review process. Natural language processing (NLP) artificial intelligence (AI) techniques have the potential to automate data extraction saving time and resources, accelerating the
review process, and enhancing the quality and reliability of extracted data. In this paper, we propose a method for using
Bing AI and Microsoft Edge as a second reviewer to verify and enhance data items first extracted by a single human reviewer. We describe a worked example of the steps involved in instructing the
Bing AI Chat tool to extract study characteristics as data items from a PDF document into a table so that they can be compared with data extracted manually. We show that this technique may provide an additional verification process for data extraction where there are limited resources available or for novice reviewers. However, it should not be seen as a replacement to already established and validated double independent data extraction methods without further evaluation and verification. Use of AI techniques for data extraction in systematic reviews should be transparently and accurately described in reports. Future research should focus on the accuracy, efficiency, completeness, and user experience of using
Bing AI for data extraction compared with traditional methods using two or more reviewers independently.