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Artificial Intelligence Software to Accelerate Screening for Living Systematic Reviews.

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Title: Artificial Intelligence Software to Accelerate Screening for Living Systematic Reviews.
Authors: Fuller-Tyszkiewicz, Matthew (AUTHOR), Jones, Allan (AUTHOR), Vasa, Rajesh (AUTHOR), Macdonald, Jacqui A. (AUTHOR), Deane, Camille (AUTHOR), Samuel, Delyth (AUTHOR), Evans-Whipp, Tracy (AUTHOR), Olsson, Craig A. (AUTHOR)
Source: Clinical Child & Family Psychology Review. Jun2026, Vol. 29 Issue 2, p191-199. 9p.
Subjects: Artificial intelligence, Computer software development, Data extraction
Abstract: Systematic and meta-analytic reviews provide gold-standard evidence but are static and outdate quickly. Here we provide performance data on a new software platform, LitQuest, that uses artificial intelligence technologies to (1) accelerate screening of titles and abstracts from library literature searches, and (2) provide a software solution for enabling living systematic reviews by maintaining a saved AI algorithm for updated searches. Performance testing was based on LitQuest data from seven systematic reviews. LitQuest efficiency was estimated as the proportion (%) of the total yield of an initial literature search (titles/abstracts) that needed human screening prior to reaching the in-built stop threshold. LitQuest algorithm performance was measured as work saved over sampling (WSS) for a certain recall. LitQuest accuracy was estimated as the proportion of incorrectly classified papers in the rejected pool, as determined by two independent human raters. On average, around 36% of the total yield of a literature search needed to be human screened prior to reaching the stop-point. However, this ranged from 22 to 53% depending on the complexity of language structure across papers included in specific reviews. Accuracy was 99% at an interrater reliability of 95%, and 0% of titles/abstracts were incorrectly assigned. Findings suggest that LitQuest can be a cost-effective and time-efficient solution to supporting living systematic reviews, particularly for rapidly developing areas of science. Further development of LitQuest is planned, including facilitated full-text data extraction and community-of-practice access to living systematic review findings. [ABSTRACT FROM AUTHOR]
Copyright of Clinical Child & Family Psychology Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Artificial Intelligence Software to Accelerate Screening for Living Systematic Reviews.
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  Data: <searchLink fieldCode="AR" term="%22Fuller-Tyszkiewicz%2C+Matthew%22">Fuller-Tyszkiewicz, Matthew</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Jones%2C+Allan%22">Jones, Allan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Vasa%2C+Rajesh%22">Vasa, Rajesh</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Macdonald%2C+Jacqui+A%2E%22">Macdonald, Jacqui A.</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deane%2C+Camille%22">Deane, Camille</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Samuel%2C+Delyth%22">Samuel, Delyth</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Evans-Whipp%2C+Tracy%22">Evans-Whipp, Tracy</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Olsson%2C+Craig+A%2E%22">Olsson, Craig A.</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Clinical+Child+%26+Family+Psychology+Review%22">Clinical Child & Family Psychology Review</searchLink>. Jun2026, Vol. 29 Issue 2, p191-199. 9p.
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  Data: <searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software+development%22">Computer software development</searchLink><br /><searchLink fieldCode="DE" term="%22Data+extraction%22">Data extraction</searchLink>
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  Data: Systematic and meta-analytic reviews provide gold-standard evidence but are static and outdate quickly. Here we provide performance data on a new software platform, LitQuest, that uses artificial intelligence technologies to (1) accelerate screening of titles and abstracts from library literature searches, and (2) provide a software solution for enabling living systematic reviews by maintaining a saved AI algorithm for updated searches. Performance testing was based on LitQuest data from seven systematic reviews. LitQuest efficiency was estimated as the proportion (%) of the total yield of an initial literature search (titles/abstracts) that needed human screening prior to reaching the in-built stop threshold. LitQuest algorithm performance was measured as work saved over sampling (WSS) for a certain recall. LitQuest accuracy was estimated as the proportion of incorrectly classified papers in the rejected pool, as determined by two independent human raters. On average, around 36% of the total yield of a literature search needed to be human screened prior to reaching the stop-point. However, this ranged from 22 to 53% depending on the complexity of language structure across papers included in specific reviews. Accuracy was 99% at an interrater reliability of 95%, and 0% of titles/abstracts were incorrectly assigned. Findings suggest that LitQuest can be a cost-effective and time-efficient solution to supporting living systematic reviews, particularly for rapidly developing areas of science. Further development of LitQuest is planned, including facilitated full-text data extraction and community-of-practice access to living systematic review findings. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Clinical Child & Family Psychology Review is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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