Review automation has the potential to revolutionize the systematic review process. By automating full-text retrieval, certain tasks such as selecting abstracts and deduplication become redundant, since the selection of the full text is superior. Additionally, tracking citations can lead to systems that retrieve the required evidence primarily through means other than searching. The main objective of review automation is to assess whether new flows have errors or not.
This process saves time by automatically sending eligible review requests, so manual review is no longer necessary. The path to a fully automated systematic review system will continue to offer a range of software applications that will directly benefit systematic reviewers, as well as others that will benefit the community in a less direct way. This study indicates that simply merging all databases into one large database improves recovery without searching multiple databases, but does not speed up systematic review. When you've set up and running Review Automation for a while, it will even tell you how much time you've saved by automating your requests.
Computer systems could automatically resolve disagreements or replace one or both reviewers to reduce the demand for resources. Semi-automated decision support systems will promote the ultimate goal of fully autonomous systematic review systems. Automation can help accelerate the process for researchers, students and librarians at many stages of review, including search, deduplication, bias assessment, and extraction. Abstrackr can continue to automatically analyze the remaining reports and thus halve the total workload that the human reviewer would otherwise have to bear.
As a result of a structured and well-thought-out test automation review process, teams can expect more robust and scalable automation. The formal drafting of the review protocol will automatically verify its coherence and logical integrity. In conclusion, review automation has the potential to drastically improve the systematic review process by streamlining certain tasks and reducing manual labor. By utilizing automated decision support systems and tracking citations, researchers can save time and resources while still ensuring accuracy in their reviews.