ArticleCampbell systematic reviews2026
Artificial Intelligence (AI) Readiness to Support Evidence Synthesis by Workflow: Findings From a Review of Reviews.
Article in Campbell systematic reviews, 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
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Evidence synthesis is crucial for informing evidence-based practice across various fields. However, the traditional methodology is resource-intensive, and its findings can be outdated before publication. There is a growing trend toward integrating automation and artificial intelligence (AI) approaches into evidence synthesis to enhance efficiency, but standardized adoption is still pending. Objective: The goal of this study is to identify peer-reviewed evidence documenting AI readiness for evidence synthesis. Methods: We searched MEDLINE, Embase, and Global Index Medicus in May 2025 to identify review articles that evaluated evidence synthesis tools. Relevant study reviews and tool reviews published in English between January 2020 and May 2025 were included in our review of reviews. Tool features and performance metrics were extracted according to stages of the evidence synthesis workflow, including search, screening, appraisal, extraction, and synthesis. Results: We included 21 studies in our review of reviews and identified 46 evidence synthesis tools. Nine tools supported all five stages of the evidence synthesis workflow, among which DistillerSR covered the most workflow-supporting features (19 out of 21). Ten of the identified tools reported sensitivity rates for AI-powered title/abstract screening, all of which achieved Conclusion: This review found peer-reviewed evidence supporting AI readiness for human-supervised automation of title/abstract screening. However, evidence documenting AI readiness for other evidence synthesis tasks remains limited. DistillerSR and EPPI-Reviewer demonstrated the broadest feature support and strong evidence for AI-powered title/abstract screening. Our study highlights the potential of AI to improve efficiency while maintaining high sensitivity in the screening stage. AI-powered screening may serve as a critical first step toward scaling rapid reviews into living evidence syntheses.
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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.