ArticleJournal of global health2026
Unmasking bias in the evidence ecosystem: a panoramic analysis of 311,751 meta-analyses using an artificial intelligence agent-based approach.
Article in Journal of global health, 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.
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Who cites it
0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Traditional secondary meta-analysis workflows are highly labour-intensive, time-consuming, and difficult to update in real time. Currently, there is a lack of comprehensive artificial intelligence frameworks capable of automating the entire meta-analysis workflow, including literature screening, data extraction, and quality assessment. Furthermore, a large-scale structured database for systematically analysing the global landscape of published meta-analyses remains unavailable. In this viewpoint, we aimed to evaluate the feasibility of large language models in automating meta-analysis workflows and develop the Meta-Analysis Screening, Transformation and Evaluation Review Agent (MASTER) agent; establish a large-scale Unified Meta-Analysis Repository (UMAR) and perform an exploratory panoramic analysis of the current evidence ecosystem; and develop an Agent-based Secondary Meta-analysis Platform (ASAP), integrating these capabilities. We subsequently applied the agent to process 311,751 meta-analysis records to establish the UMAR database. Building upon these resources, we developed the ASAP platform to support multimodal, automated meta-analysis workflows. In benchmark evaluations, the MASTER agent demonstrated high accuracy and stability in performing core automated meta-analysis tasks. The ASAP platform enabled automated literature retrieval, quality assessment, data extraction, and visualisation generation through predefined workflows. Here, we provide an initial exploration of the technical feasibility and scalability of artificial intelligence-driven automated meta-analysis.
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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.