ArticleSystematic reviews2025
ReviewGenie: a novel automated system for systematic reviews-an exploratory study in speech and language disorders.
Article in Systematic reviews, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
backgroundSystematic reviews (SRs) are a cornerstone in providing high-quality evidence that guides policy and practice across various disciplines. Despite their critical role, SRs require substantial financial investment and are constrained by time-consuming manual processes. Existing solutions primarily focus on semi-automating the title and abstract screening stages, yet these approaches still face limitations in terms of efficiency and practicality. The SR process comprises several stages beyond abstract screening, each of which is resource-intensive. To overcome these challenges, this paper introduces ReviewGenie, a novel system that automates SR stages up to and including abstract screening, utilizing artificial intelligence.
methodThe SR process involves eight key stages, beginning with the definition of search keywords and the selection of target databases, and culminating in full screening. While the initial and final stages require human expertise, the intermediate stages can be automated. ReviewGenie automates all intermediary stages, including database searching, data retrieval, cleaning, deduplication, filtering, and abstract screening. The system is domain-agnostic, as evidenced by a case study focused on databases related to speech and language disorders.
resultsReviewGenie significantly reduces the workload across various stages of the SR process, delivering notable time and cost savings while enhancing efficiency and accuracy. In the case study, where the article-fetching stage involved tens of thousands of publications, ReviewGenie achieved a 2.62% improvement in duplicate detection in less than a second, compared to the 1 to 3 h typically required for manual deduplication of 100 records. This process included cleaning abstracts before removing duplicates. Additionally, ReviewGenie reduced the number of articles from 28,674 to 3520 using an automatic filtering approach executed in seconds. This substantial reduction underscores the effectiveness of our automated method in preparing datasets for the abstract screening stage. Moreover, the artificial intelligence-driven abstract screening method resulted in cost savings exceeding $6230 compared to manual methods.
conclusionsReviewGenie represents a significant advancement in reducing the burden on researchers conducting comprehensive systematic reviews. By automating intermediate stages, ReviewGenie enhances efficiency, accuracy, and cost-effectiveness, establishing itself as an indispensable tool for SRs across various disciplines.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.