ArticleBMJ open2025
Comparing the accuracy of AI-assisted data extraction versus human double extraction in evidence synthesis: a randomised controlled trial protocol.
Article in BMJ open, 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
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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.
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Who cites it
1 citing paper in PubMed.
- Evaluating large language models for automated meta-analysis generation.Eye (London, England) · 2026Article
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionTraditional data extraction strategies, such as human double extraction, are both time consuming and labour-intensive. Artificial intelligence (AI) has emerged as a promising tool for facilitating data extraction. However, it is not yet suitable as a standalone solution. We will conduct a randomised controlled trial (RCT) to compare the efficiency and accuracy of the AI-human data extraction strategy with human double extraction. METHODS AND ANALYSIS: This study is designed as a randomised, controlled, parallel trial. Participants will be randomly assigned to either the AI group or the non-AI group at a 1:2 allocation ratio. The AI group will use a hybrid approach that combines AI extraction followed by human verification by the same participant, while the non-AI group will use human double extraction. Data will be collected for two tasks: event count and group size. Ten RCTs will be selected from an established database that analysed data extraction errors in systematic reviews of sleep medicine. The primary outcome measure will be the percentage of correct extractions by both groups for each data extraction task. ETHICS AND DISSEMINATION: The trial is approved by the Ethics Council of Anhui Medical University (No. 81250507). We plan to publish the main results as an academic publication in an international peer-reviewed journal in 2026. TRIAL REGISTRATION NUMBER: Chinese Clinical Trial Register (Identifier: ChiCTR2500100393).
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