ArticleCancer innovation2026
Evaluating the Performance of Covidence's Artificial Intelligence-Based Screening Function in Cancer Systematic Reviews.
Article in Cancer innovation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Systematic reviews (SRs), often conducted for evidence synthesis, require extensive multistage screening, with Stage-I title/abstract screening being especially time consuming. Covidence systematic review software, a widely used SR platform, has offered artificial intelligence (AI)-assisted Stage-I screening function since 2022. However, this function's performance has not yet been formally compared across heterogeneous, large-scale oncology-focused SRs. Methods: We selected two completed SRs conducted by the Program in Evidence-based Care (PEBC), Ontario, Canada. The first SR (Axilla-BC, Results: When pooling all subset sizes and the Conclusions: In two large oncology-SRs, Covidence's AI-assisted Stage-I screening function demonstrated meaningful potential to reduce reviewer workload and screening time. Nonetheless, Covidence's performance varied notably between reviews, suggesting a possible association between the results and the characteristics of the SR, including topic, methodological complexity, relevance rate, and inclusion rate. Careful human oversight remains essential to ensure that no potentially finally included studies are missed in evidence synthesis, particularly in nuanced or methodologically complex reviews.
Identifiers
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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.