ArticleSystematic reviews2025
Accelerating the pace and accuracy of systematic reviews using AI: a validation study.
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 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- Moving towards acceleration with accountability: a conceptual framework for AI-assisted systematic reviews.Global epidemiology · 2026Article
- Artificial Intelligence Resources for the Screening of Titles and Abstracts in Systematic Reviews: A Scoping Review.Cochrane evidence synthesis and methods · 2026Review
- Leveraging prompt-driven generative AI for systematic reviews in digital psychiatry: A stage-matched comparative proof-of-concept for healthcare researchers and clinicians.PLOS digital health · 2026Article
- A Machine Learning and Large Language Model Tool for Systematic Literature Reviews of Health Economic Evidence: A Validation Study.PharmacoEconomics · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundArtificial intelligence (AI) can greatly enhance efficiency in systematic literature reviews and meta-analyses, but its accuracy in screening titles/abstracts and full-text articles is uncertain.
objectivesThis study evaluated the performance metrics (sensitivity, specificity) of a GPT-4 AI program, Review Copilot, against human decisions (gold standard) in screening titles/abstracts and full-text articles from four published systematic reviews/meta-analyses. RESEARCH
designParticipant data from four already-published systematic literature reviews were used for this validation study. This was a study comparing Review Copilot to human decision-making (gold standard) in screening titles/abstracts and full-text articles for systematic reviews/meta-analyses. The four studies that were used in this study included observational studies and randomized control trials. Review Copilot operates on the OpenAI, GPT-4 server. We examined the performance metrics of Review Copilot to include and exclude titles/abstracts and full-text articles as compared to human decisions in four systematic reviews/meta-analyses. Sensitivity, specificity, and balanced accuracy of title/abstract and full-text screening were compared between Review Copilot and human decisions.
resultsReview Copilot's sensitivity and specificity for title/abstract screening were 99.2% and 83.6%, respectively, and 97.6% and 47.4% for full-text screening. The average agreement between two runs was 95.4%, with a kappa statistic of 0.83. Review Copilot screened in one-quarter of the time compared to humans.
conclusionsAI use in systematic reviews and meta-analyses is inevitable. Health researchers must understand these technologies' strengths and limitations to ethically leverage them for research efficiency and evidence-based decision-making in health.
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Registered trials
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