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This summary is machine-generated.

This study introduces an AI tool, AutoLit, that supports all stages of systematic literature reviews (SLRs) with human oversight. The AI tool demonstrates significant time savings and high accuracy in search, screening, and extraction for robust evidence synthesis.

Keywords:
artificial intelligenceevidence synthesishuman‐in‐the‐loopmeta‐analysissystematic literature review

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Area of Science:

  • Artificial Intelligence in Scientific Research
  • Systematic Literature Review Methodologies
  • Evidence Synthesis and Meta-Analysis

Background:

  • Systematic literature reviews (SLRs) are crucial for evidence synthesis but are time-consuming.
  • Existing AI tools support only individual SLR stages, not the complete workflow.
  • Ensuring the quality and accuracy of SLR findings necessitates expert oversight.

Purpose of the Study:

  • To present a comprehensive methodology for conducting SLRs using an integrated AI tool with human-in-the-loop curation.
  • To validate the AI tool's performance against expert reviews and quantify time savings.
  • To outline approaches for maintaining best practices in AI-assisted SLRs.

Main Methods:

  • Utilized AutoLit software, integrating AI for search strategy generation, dual screening, and evidence extraction.
  • Incorporated manual critical appraisal and AI-powered network meta-analysis.
  • Conducted validations comparing AI performance to human experts, assessing time savings and 'rapid review' alternatives.

Main Results:

  • AI-driven search strategy generation achieved 76.8-79.6% recall.
  • Supervised machine learning for screening reached 82-97% recall.
  • Evidence extraction (PICOs) showed an F1 score of 0.74, with study type, location, and size accuracy at 74%, 78%, and 91% respectively.
  • Reported 50% time savings in abstract screening and 70-80% in qualitative extraction.

Conclusions:

  • AI systems, with human oversight, can effectively support high-quality systematic literature reviews.
  • Transparency, replicability, and expert involvement are key to successful AI-assisted SLR.
  • The AutoLit methodology offers a framework for efficient and reliable evidence synthesis.