The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Assessing the article screening efficiency of artificial intelligence for Systematic Reviews
Abstract
Objectives Artificial intelligence (AI) tools utilizing machine learning (ML) have gained increasing utility in medicine and academia as a means of enhancing efficiency. ASReview is one such AI program designed to streamline the systematic review process through the automated prioritization of relevant articles for screening. This study examined the screening efficiency of ASReview when conducting systematic reviews and the potential factors that could influence its efficiency. Methods Six distinct topics within the field of periodontics were searched in PubMed and Web of Science to obtain articles for screening within ASReview. Through a "training" process, relevant and irrelevant articles were manually incorporated to develop "prior knowledge" and facilitate ML optimization. Screening was then conducted following ASReview's algorithmically-generated relevance rankings. Screening efficiency was evaluated based on the normalized number of articles not requiring detailed review and on the total time expenditure. Results Across the six topics, an average of 60.2 % of articles did not warrant extensive screening, given that all relevant articles were discovered within the first 39.8 % of publication reviewed. No significant variations in efficiencies were observed with differing methods of assembling prior knowledge articles or via modifications in article ratios and numbers. Conclusions On average, ASReview conferred a 60.2 % improvement in screening efficiency, largely attribu…
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“Therefore, it is quite questionable whether the added value of ASReview in terms of reducing the screening workload, as presented in individual studies [9, 10], is achievable if there is no reliable criterion for stopping the screening.”
From When to stop reviewing: validation of stop criteria in ASReview (2026) · cites this paper as [10]
In the citing paper
“Various tests of ASReview have shown that potentially relevant studies tend to be displayed at the beginning of the screening and that few or no relevant studies appear further down the ranked data set [9, 10].”
From When to stop reviewing: validation of stop criteria in ASReview (2026) · cites this paper as [10]
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Cites
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Ray, P. P. (2023) · Internet of Things and Cyber-Physical Systems · doi:10.1016/j.iotcps.2023.04.003 · Cited by 2,463
An open source machine learning framework for efficient and transparent systematic reviews
van de Schoot, R., de Bruin, J., Schram, R., Zahedi, P., de Boer, J., Weijdema, F., Kramer, B., Huijts, M., Hoogerwerf, M., Ferdinands, G., et al. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-020-00287-7 · Cited by 1,157
Alshater, M. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4312358 · Cited by 304
Ferdinands, G., Schram, R., de Bruin, J., Bagheri, A., Oberski, D. L., Tummers, L., Teijema, J. J., & van de Schoot, R. (2023) · Systematic Reviews · doi:10.1186/s13643-023-02257-7 · Cited by 96
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Cited by
When to stop reviewing: validation of stop criteria in ASReview
Kempny, C., Annac, K., Wahidie, D., Yilmaz-Aslan, Y., & Brzoska, P. (2026) · BMC Medical Research Methodology · doi:10.1186/s12874-026-02866-5 · Cited by 5
Metadata
- Authors
- Chan, Y.-T., Abad, J. E., Dibart, S., & Kernitsky, J. R.
- Venue
- Journal of Dentistry
- Publisher
- Elsevier BV
- Published
- 2024-10
- Volume
- 149
- Pages
- 105259
- ISSN
- 0300-5712
- Cited by
- 23
- References
- 5
- Language
- en
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