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…
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Revolutionizing healthcare: the role of artificial intelligence in clinical practice
Alowais, S. A., Alghamdi, S. S., Alsuhebany, N., Alqahtani, T., Alshaya, A. I., Almohareb, S. N., Aldairem, A., Alrashed, M., Bin Saleh, K., Badreldin, H. A., et al. (2023) · BMC Medical Education · doi:10.1186/s12909-023-04698-z · Cited by 3,146
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
Research works printed but not linked: 13
- Basu, 2020, Artificial Intelligence: how is It Changing Medical Sciences and Its Future?, Indian J. Dermatol., 65, 365, 10.4103/ijd.IJD_421_20 · DOI
- Agrawal, 2022, Artificial Intelligence in Dentistry: past, Present, and Future, Cureus., 14
- Rolfes, 2023, Artificial Intelligence in Reproductive Medicine – an Ethical Perspective, Geburtshilfe Frauenheilkd [Internet], 83, 106, 10.1055/a-1866-2792 · DOI
- EHR, 2023, Revolutionary Partnership in Behavioral Health: nabla Copilot and Opus EHR Join Forces to Enhance Clinical Practice with Powerful AI, GlobeNewswire News Room
- Zinovia, 2023, Current Progress and Challenges of Using Artificial Intelligence in Clinical Dentistry—A Narrative Review, J. Clin. Med., 12, 7378, 10.3390/jcm12237378 · DOI
- Talib, 2022, Flapless dental implant placement using a recently developed haptic robotic system, British J. Oral Maxillofacial Surgery, 60, 1273, 10.1016/j.bjoms.2022.05.008 · DOI
- Alam, 2023, Artificial Intelligence in Global World: a Case Study of Grammarly as e-Tool on ESL Learners’ Writing of Darul Uloom Nadwa, International J. Inf. Education Technology, 13, 1741, 10.18178/ijiet.2023.13.11.1984 · DOI
- Alotaibi, 2023, The Impact Of AI-Powered Grammarly on Enhancing Grammar Proficiency Among Saudi EFL Students, Remittances Review, 8
- Halgamuge, 2017, The use and analysis of anti-plagiarism software: turnitin tool for formative assessment and feedback, Computer Appl. Engineering Education, 25, 895, 10.1002/cae.21842 · DOI
- AlAfnan, 2023, Do Artificial Intelligence Chatbots Have a Writing Style? An Investigation into the Stylistic Features of ChatGPT-4, J. Artificial Intell. Technology, 3
- Rudolph, 2023, JALT Postconference Publication - Issue 2022.1; August 2023, J. Applied Learn. Teaching, 1
- van Dijk, 2023, Artificial intelligence in systematic reviews: promising when appropriately used, BMJ Open., 13, 10.1136/bmjopen-2023-072254 · DOI · Paper page
- Muthu, 2022, The efficiency of machine learning-assisted platform for article screening in systematic reviews in orthopaedics, Int. Orthop., 47, 551, 10.1007/s00264-022-05672-y · DOI · Paper page
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Assessing the article screening efficiency of artificial intelligence for Systematic Reviews. https://app.cochat.ai/papers/10.1016/j.jdent.2024.105259
- The paper
- Chan, Y.-T., Abad, J. E., Dibart, S., & Kernitsky, J. R. (2024). Assessing the article screening efficiency of artificial intelligence for Systematic Reviews. Journal of Dentistry. https://doi.org/10.1016/j.jdent.2024.105259
