meta-pipe: An LLM-agent pipeline for end-to-end automated systematic review and meta-analysis
arXiv:2606.28363v1 Announce Type: cross Abstract: Objective: To describe the architecture and design rationale of meta-pipe, an open-source large language model (LLM)-agent pipeline that integrates the complete systematic review and meta-analysis (SR/MA) workflow -- from literature search through statistical analysis, manuscript generation, and quality assurance -- with mandatory human oversight at critical decision points. Study Design and Setting: We developed a 10-stage modular pipeline integrating Claude (Anthropic; Opus 4 for reasoning, Haiku 3.5 for classification) for LLM-assisted scree
The rapid advancement of large language models and multi-agent systems is enabling automation of complex, cognitive tasks previously thought to require deep human expertise, making such pipelines feasible now.
This development indicates a significant step towards automating highly specialized white-collar work, impacting research methodologies, publication cycles, and the efficiency of knowledge creation across various fields.
The systematic review and meta-analysis process, traditionally labor-intensive and time-consuming, can now be largely automated with mandatory human oversight, drastically changing research throughput and access.
- · AI research and development
- · Academics and researchers
- · Scientific publishing platforms
- · Open-source AI community
- · Manual systematic review service providers
- · Legacy research methodologies
- · Junior researchers performing manual review
Increased pace and volume of systematic reviews and meta-analyses, potentially leading to faster scientific consensus.
Democratization of complex research methods, allowing more individuals and institutions to conduct high-quality evidence synthesis.
Potential for an 'AI-vs-AI' research arms race, where AI-generated reviews are challenged or validated by other AI systems, evolving the nature of scientific discourse.
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