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Automated, Quantitative Cognitive/Behavioral Screening of Mice: For Genetics, Pharmacology, Animal Cognition and Undergraduate Instruction

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Artificial Intelligence Software to Accelerate Screening for Living Systematic Reviews.

Matthew Fuller-Tyszkiewicz^1,2, Allan Jones³, Rajesh Vasa³

¹School of Psychology, Faculty of Health, Deakin University, Geelong, Australia. matthewf@deakin.edu.au.

Clinical Child and Family Psychology Review

|April 18, 2025

Summary

LitQuest, a new AI software, speeds up literature screening for systematic reviews. It helps create living systematic reviews, saving time and costs in rapidly evolving scientific fields.

Keywords:

Accuracy Artificial intelligence Efficiency Machine learning Systematic reviews

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Area of Science:

Bibliometrics
Information Science
Artificial Intelligence in Research

Background:

Systematic and meta-analytic reviews are crucial for evidence-based practice.
Traditional reviews are static and become outdated rapidly.
Living systematic reviews offer a solution for dynamic evidence synthesis.

Purpose of the Study:

To evaluate the performance of LitQuest, an AI-powered software platform.
To assess LitQuest's efficiency and accuracy in accelerating literature screening.
To determine LitQuest's utility in supporting living systematic reviews.

Main Methods:

LitQuest performance was tested using data from seven systematic reviews.
Efficiency was measured by the proportion of literature requiring human screening.
Algorithm performance was assessed using work saved over sampling (WSS) for recall.
Accuracy was determined by the proportion of incorrectly classified papers.

Main Results:

On average, 36% of literature required human screening, ranging from 22% to 53%.
LitQuest demonstrated 99% accuracy with 95% interrater reliability.
0% of titles/abstracts were incorrectly assigned by the algorithm.

Conclusions:

LitQuest offers a cost-effective and time-efficient solution for systematic reviews.
The software is particularly beneficial for living systematic reviews in fast-paced scientific areas.
Future developments include full-text extraction and community access to findings.