2021
DOI: 10.1186/s12874-021-01451-2
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Guidance for using artificial intelligence for title and abstract screening while conducting knowledge syntheses

Abstract: Background Systematic reviews are the cornerstone of evidence-based medicine. However, systematic reviews are time consuming and there is growing demand to produce evidence more quickly, while maintaining robust methods. In recent years, artificial intelligence and active-machine learning (AML) have been implemented into several SR software applications. As some of the barriers to adoption of new technologies are the challenges in set-up and how best to use these technologies, we have provided … Show more

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Cited by 41 publications
(39 citation statements)
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“…Additionally, the results show that authors should involve translation software or translators in the conduct of rapid and systematic reviews including non-English articles, either internally or through a research network. Generally, the growing use of automation software may prevent the false exclusion of studies [ 19 ]. This would require providing a clean, high-quality initial data set to train the algorithm, ensuring that any duplicates with conflicting decisions are removed and prespecifying records correctly as includes or excludes [ 19 ].…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…Additionally, the results show that authors should involve translation software or translators in the conduct of rapid and systematic reviews including non-English articles, either internally or through a research network. Generally, the growing use of automation software may prevent the false exclusion of studies [ 19 ]. This would require providing a clean, high-quality initial data set to train the algorithm, ensuring that any duplicates with conflicting decisions are removed and prespecifying records correctly as includes or excludes [ 19 ].…”
Section: Discussionmentioning
confidence: 99%
“…Generally, the growing use of automation software may prevent the false exclusion of studies [ 19 ]. This would require providing a clean, high-quality initial data set to train the algorithm, ensuring that any duplicates with conflicting decisions are removed and prespecifying records correctly as includes or excludes [ 19 ].…”
Section: Discussionmentioning
confidence: 99%
“… 64 For stage 1, we will use Distiller SR’s artificial intelligence (AI) active-machine learning feature to prioritise title and abstract screening of citations. 65 66 This method has been validated. 66 This active-machine learning feature will allow us to perform prioritised screening, as a relevance score will be generated for each citation during an initial training exercise on a sample of approximately 200 citations; this feature will continue to learn throughout the stage 1 screening process, presenting reviewers with the most relevant citations first.…”
Section: Methods and Analysismentioning
confidence: 99%
“…A human reviewer will screen all citations excluded by the AI reviewer, with any conflicts to be resolved by two human reviewers. Our approach is in alignment with recent guidance for the use of AI in knowledge syntheses 46. All potentially relevant full-text articles will be screened by independent reviewers in duplicate for eligibility, with conflicts to be resolved through discussion or consultation with a third reviewer until consensus is reached.…”
Section: Methods and Analysismentioning
confidence: 99%