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. |
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| 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] |
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| Database: | Psychology and Behavioral Sciences Collection |
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| 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] |
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| ISSN: | 10964037 |
| DOI: | 10.1007/s10567-025-00519-5 |
