AutoSynthesis能自动完成整条元分析流水线，效果媲美专家手工操作，让证据综合不再依赖人工
AutoSynthesis是一个端到端多智能体系统，输入自然语言研究问题后，自动生成搜索策略、检索文献、筛选28项研究、提取20多个定量声明，并计算标准化效应量进行随机效应元分析。系统支持异质性分析和偏倚风险评估，输出符合PRISMA指南的报告。其汇集效应估计与专家手动元分析的Hedges' g结果高度一致，表明自动元分析可规模化。
AutoSynthesis: An agentic system for automated meta-analysis
Evidence synthesis is crucial for turning primary research into reliable knowledge for science, medicine, education, and policy. Yet, quantitative evidence synthesis remains largely manual and difficult to scale. Here, we introduce AutoSynthesis, an end-to-end multi-agent system for automated meta-analysis. Given a research question in natural language, AutoSynthesis formulates a search strategy, retrieves scientific literature, screens candidate studies, assesses full-text eligibility, extracts quantitative statistics, computes standardized effect sizes, and finally performs random-effects meta-analysis. AutoSynthesis further supports heterogeneity analysis to examine how effect sizes vary across moderators, as well as risk-of-bias assessment. As output, AutoSynthesis produces a transparent report aligned with PRISMA guidelines. In our application, AutoSynthesis screened over 28 studies and extracted more than 20 quantitative claims. The pooled effect estimates produced by AutoSynthesis are similar to Hedges' $g$ of expert-conducted meta-analyses, indicating close agreement with manual evidence synthesis. Together, these results show that AutoSynthesis can make quantitative evidence synthesis more scalable, thereby supporting evidence-based decision-making across disciplines.