Methods for using Bing's AI‐powered search engine for data extraction for a systematic review

Abstract

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.

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“…Advancement in natural language generation also allow for further summarisation and contextualisation of the studied articles. Hill et al (2023) examined the use of Bing for extraction, showcasing the potential of search engines as tools for efficient information retrieval. Alshami et al (2023) tested the capabilities of the Chat-GPT-3.5 and highlighted that "this innovative language model holds promise for various domains, including systematic reviews" (p. 30).…”