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Source note2 min read

AutoSynthesis: An agentic system for automated meta-analysis

Source

arXiv

arxiv.org/abs/2607.15247v1

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Source

Plain-Language Brief

A new system called AutoSynthesis has been developed to automate the process of meta-analysis in various fields such as science, medicine, education, and policy. The system takes a research question in natural language, searches for relevant studies, assesses their eligibility, extracts quantitative statistics, computes standardized effect sizes, performs random-effects meta-analysis, and produces a transparent report aligned with PRISMA guidelines. The results show that AutoSynthesis can produce similar pooled effect estimates to those of expert-conducted meta-analyses.

What the Document Says

  • AutoSynthesis is an end-to-end multi-agent system for automated meta-analysis.
  • It takes a research question in natural language as input.
  • It formulates a search strategy, retrieves scientific literature, screens candidate studies, assesses full-text eligibility, extracts quantitative statistics, computes standardized effect sizes, and performs random-effects meta-analysis.
  • It supports heterogeneity analysis and risk-of-bias assessment.
  • As output, it produces a transparent report aligned with PRISMA guidelines.
  • In the application, it 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.
  • This indicates close agreement with manual evidence synthesis.

Key Concepts Mentioned

  • AutoSynthesis
  • meta-analysis
  • quantitative statistics
  • standardized effect sizes
  • random-effects meta-analysis
  • heterogeneity analysis
  • risk-of-bias assessment
  • PRISMA guidelines

Claims Made by the Document

  • AutoSynthesis can make quantitative evidence synthesis more scalable.
  • The pooled effect estimates produced by AutoSynthesis are similar to Hedges' $g$ of expert-conducted meta-analyses.

Evidence Used

  • AutoSynthesis produces a transparent report aligned with PRISMA guidelines.
  • It 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.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

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.