Unmasking bias in the evidence ecosystem: a panoramic analysis of 311,751 meta-analyses using an artificial intelligence agent-based approach

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.