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<https://discovery.ucl.ac.uk/id/eprint/10082338> <http://purl.org/ontology/bibo/abstract> "Background: Although many aspects of systematic reviews use computational tools, systematic reviewers have\r\nbeen reluctant to adopt machine learning tools.\r\nDiscussion: We discuss that the potential reason for the slow adoption of machine learning tools into systematic\r\nreviews is multifactorial. We focus on the current absence of trust in automation and set-up challenges as major\r\nbarriers to adoption. It is important that reviews produced using automation tools are considered non-inferior or\r\nsuperior to current practice. However, this standard will likely not be sufficient to lead to widespread adoption. As\r\nwith many technologies, it is important that reviewers see “others” in the review community using automation\r\ntools. Adoption will also be slow if the automation tools are not compatible with workflows and tasks currently\r\nused to produce reviews. Many automation tools being developed for systematic reviews mimic classification\r\nproblems. Therefore, the evidence that these automation tools are non-inferior or superior can be presented using\r\nmethods similar to diagnostic test evaluations, i.e., precision and recall compared to a human reviewer. However,\r\nthe assessment of automation tools does present unique challenges for investigators and systematic reviewers,\r\nincluding the need to clarify which metrics are of interest to the systematic review community and the unique\r\ndocumentation challenges for reproducible software experiments.\r\nConclusion: We discuss adoption barriers with the goal of providing tool developers with guidance as to how to\r\ndesign and report such evaluations and for end users to assess their validity. Further, we discuss approaches to\r\nformatting and announcing publicly available datasets suitable for assessment of automation technologies and\r\ntools. Making these resources available will increase trust that tools are non-inferior or superior to current practice.\r\nFinally, we identify that, even with evidence that automation tools are non-inferior or superior to current practice,\r\nsubstantial set-up challenges remain for main stream integration of automation into the systematic review process."^^<http://www.w3.org/2001/XMLSchema#string> .
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