Accuracy and Efficiency of Automated or Artificial Intelligence Tools in Systematic Literature Reviews: A Rapid Systematic Literature Review
Authors: Alex Jenkins, Shona Lang, Emily Hardy, Janine Ross
Presenter: Shona Lang
Presentation Time: Wednesday 20th November, 9.00am–11.30am
In this short video, Emily Hardy (Consultant – Systematic Review) talks us through the team’s research:
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References for our poster:
1. Cichewicz A, Slim M, Deshpande S. MSR163 Artificial Intelligence (AI)-Based Screening: Exploration of Differences in Two Health Technology Assessment (HTA)-Compliant Systematic Literature Reviews (SLRS). Value in Health. 2023;26(12),S425.
2. Li J, Kabouji J, Bouhadoun S, Khalil S, Mynard N, et al. Sensitivity and specificity of alternative screening methods for systematic reviews using text mining tools. JCE. 2023;62,72-80.
3. Reddy SM, Patel S, Weyrich M, et al. Comparison of a traditional systematic review approach with review-of-reviews and semi-automation as strategies to update the evidence. Syst Rev. 2020;9,243.
4. Tsou AY, Treadwell JR, Erinoff E, et al. Machine learning for screening prioritization in systematic reviews: comparative performance of Abstrackr and EPPI-Reviewer. Syst Rev. 2020; 9,73.
5. dos Reis AHS, de Oliveira ALM, Fritsch C, et al. Usefulness of machine learning softwares to screen titles of systematic reviews: a methodological study. Syst Rev. 2023;12,68.
6. Gates A, Guitard S, Pillay J, et al. Performance and usability of machine learning for screening in systematic reviews: a comparative evaluation of three tools. Syst Rev. 2019;8,278.
