Tools to support the automation of systematic reviews: a scoping review

Abstract

Objective: The objectives of this scoping review are to identify the reliability and validity of the available tools, their limitations and any recommendations to further improve the use of these tools.

Study design: A scoping review methodology was followed to map the literature published on the challenges and solutions of conducting evidence synthesis using the JBI scoping review methodology.

Results: A total of 47 publications were included in the review. The current scoping review identified that LitSuggest, Rayyan, Abstractr, BIBOT, R software, RobotAnalyst, DistillerSR, ExaCT and NetMetaXL have potential to be used for the automation of systematic reviews. However, they are not without limitations. The review also identified other studies that employed algorithms that have not yet been developed into user friendly tools. Some of these algorithms showed high validity and reliability but their use is conditional on user knowledge of computer science and algorithms.

Conclusion: Abstract screening has reached maturity; data extraction is still an active area. Developing methods to semi-automate different steps of evidence synthesis via machine learning remains an important research direction. Also, it is important to move from the research prototypes currently available to professionally maintained platforms.

Keywords: Abstract screening; Artificial intelligence; Automation; Machine learning; Reliability; Systematic review.