MacAma: Multi‐AI Agent as a Co‐Scientist for Automated Meta‐Analysis

ABSTRACT Meta‐analysis is fundamental to evidence‐based medicine, yet traditional workflows remain labor‐intensive and susceptible to bias. Although LLM‐based research agents offer opportunities for workflow automation, they often lack the data fidelity and methodological traceability required for rigorous quantitative evidence synthesis, particularly when parsing multimodal scientific charts. To address this challenge, we introduce MacAma, a semi‐automated multi‐agent framework for protocol‐constrained and human‐verifiable meta‐analysis. MacAma operationalizes selected PRISMA 2020 reporting items, PICOS‐based eligibility logic, and SYRCLE risk‐of‐bias domains as structured prompts, decision rules, output fields, and audit records. Critically, MacAma adopts a risk‐aware automation strategy: Lower risk, repetitive, and protocol‐driven tasks, such as literature screening and drafting, are delegated to AI agents, whereas high‐impact steps that directly affect effect‐size estimation and statistical conclusions, such as quantitative chart‐data extraction, remain subject to expert verification. In a preclinical radiotherapy case study evaluating tumor‐related immune outcomes and metastatic potential mediated by circulating tumor cells, MacAma achieved competitive screening performance in the evaluated benchmark and reduced the manual screening burden by over 80% within the current workflow. The case study further demonstrates how structured agent outputs, predefined criteria, and audit records can support transparent screening, data extraction, statistical synthesis, and manuscript drafting . These results suggest that MacAma may provide a scalable and auditable framework for AI‐assisted meta‐analysis, although important limitations remain in full‐text access, quantitative chart data extraction, and expert interpretation of heterogeneity. MacAma is open‐source and available at https://github.com/YilinYuan/MacAma .