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AI pipelines automate systematic reviews and meta-analyses, cutting costs

Two new research papers detail the development of automated pipelines for systematic reviews and meta-analyses using multiple AI agents. The first, LUMEN, automates six phases of the process, achieving directional agreement with published meta-analyses at a cost of $19-$29 per review. It highlights that while multi-agent designs improve data extraction, they can hinder screening. The second system, meta-pipe, integrates a complete workflow including manuscript generation and overclaim detection, with an estimated API cost of $15-$30 per review. Both systems emphasize the feasibility of AI-assisted evidence synthesis, with meta-pipe noting that formal validation is still underway. AI

IMPACT These systems demonstrate the potential for AI to significantly reduce the time and cost of evidence synthesis in scientific research.

RANK_REASON Two academic papers published on arXiv describing new AI systems for systematic reviews and meta-analyses.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

AI pipelines automate systematic reviews and meta-analyses, cutting costs

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Yen-Hsun Huang (Department of Education, Taipei Veterans General Hospital, Taipei, Taiwan), Yu-Shiou Lin (Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan) ·

    LUMEN: Cost-Transparent Multi-Agent Pipeline for Automated Systematic Review and Meta-Analysis

    arXiv:2606.28362v1 Announce Type: cross Abstract: Systematic reviews and meta-analyses (SR/MA) remain the gold standard for evidence synthesis, yet completing one typically requires 67 weeks and substantial expert effort. Recent large language model (LLM) systems have demonstrate…

  2. arXiv cs.AI TIER_1 English(EN) · Hsieh-Ting Lin, Jiunn-Tyng Yeh ·

    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 literatu…