OBJECTIVES: Systematic Literature Review (SLR) is considered the gold standard for evidence synthesis. In SLR, data extraction is the most time-consuming, as it involves selecting and extracting needed data from the studies in dignified format. To enhance the accuracy and speed of extraction, the tools have integrated Artificial Intelligence (AI). So, we conducted research to analyze the AI-integrated extraction features of the available tools.

METHODS: We identified the list of AI tools that assist in conducting SLR from previously published literature and a Google search with a group of keywords. After the retrieval of the list of tools, individual websites of the tool have been assessed for their integration with AI for data extraction.

RESULTS: We retrieved the names of 26 AI-based SLR tools from our search. Out of the 26 tools included, 24 have features to perform data extractions, but only 6 tools (Iris.ai, Nested Knowledge, Pitts.ai, RobotReviewer, Laser.ai, and Easy SLR) integrated AI into the data extraction. The Nested Knowledge and Pitts AI tools have integrated with ChatGPT for data extraction. Iris AI provides that it will automatically extract and systematize any key data points from text and tables into a table layout of your own design, while RobotReviewer is used for automatic data extraction from CT.gov of qualitative variables. Most of the AI-integrated tools have explored the qualitative extraction of data like demographics (title, author, study start and end, male and female, etc.) and are still in the development of quantitative extraction.

CONCLUSIONS: As the data reported in each study differs and the complexity varies, tools have explored only the extraction of qualitative information and are in the beta stage of exploring the quantitative part. It is evident that human intelligence plays a pivotal role in data extraction as AI tools need supervision.