ArticleCochrane evidence synthesis and methods2025
Artificial Intelligence and Machine Learning to Improve Evidence Synthesis Production Efficiency: An Observational Study of Resource Use and Time-to-Completion.
Article in Cochrane evidence synthesis and methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Evidence-to-Decision Frameworks: Enhancing the Quality and Rigour of Guidelines and Recommendations.Clinical and public health guidelines · 2026Article
- Don't Stop Me Now, `Cause I'm Having a Good Time Screening: Evaluation of Stopping Methods for Safe Use of Priority Screening in Systematic Reviews.Cochrane evidence synthesis and methods · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Evidence syntheses are crucial in healthcare and elsewhere but are resource-intensive, often taking years to produce. Artificial intelligence and machine learning (AI/ML) tools may improve production efficiency in certain review phases, but little is known about their impact on entire reviews. Methods: We performed prespecified analyses of a convenience sample of eligible healthcare- or welfare-related reviews commissioned at the Norwegian Institute of Public Health between August 1 2020 (first commission to use AI/ML) and January 31 2023 (administrative cut-off). The main exposures were AI/ML use following an internal support team's recommendation versus no use. Ranking (e.g., priority screening), classification (e.g., study design), clustering (e.g., documents), and bibliometric analysis (e.g., OpenAlex) tools were included, but we did not include or exclude specific tools. Generative AI tools were not widely available during the study period. The outcomes were resources (person-hours) and time from commission to completion (approval for delivery, including peer review; weeks). Analyses accounted for nonrandomized assignment and censored outcomes (reviews ongoing at cut-off). Researchers classifying exposures were blinded to outcomes. The statistician was blinded to exposure. Results: Among 39 reviews, 7 (18%) were health technology assessments versus systematic reviews, 19 (49%) focused on healthcare versus welfare, 18 (46%) planned meta-analysis, and 3 (8%) were ongoing at cut-off. AI/ML tools were used in 27 (69%) reviews. Reviews that used AI/ML as recommended used more resources (mean 667 vs. 291 person-hours) but were completed slightly faster (27.6 vs. 28.2 weeks). These differences were not statistically significant (relative resource use 3.71; 95% CI: 0.36-37.95; Conclusions: Associations between AI/ML use and the outcomes remains uncertain. Multicenter studies or meta-analyses may be needed to determine if these tools meaningfully reduce resource use and time to produce evidence syntheses.
Indexed as
Identifiers
What Socratic holds
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 Socratic graph.