12,637 papers · updated 10 Oct 2026livingmeta.ai
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Research theme

Systematic Review Automation

The Systematic Review Automation theme comprises 305 papers in this corpus published between 1974 and 2026. Work here is dominated by Design Science, Literature Review, Empirical Study. 5 open research gaps have been surfaced in this area.

Methodology profile

  • Design Science79 (26%)
  • Literature Review55 (18%)
  • Empirical Study36 (12%)
  • Experimental23 (8%)
  • Conceptual22 (7%)
  • Case Study18 (6%)

Research domains

  • Literature Discovery207 (68%)
  • Knowledge Synthesis82 (27%)
  • Scholarly Infrastructure7 (2%)
  • Peer Review4 (1%)
  • Multi-Domain2 (1%)
  • Doctoral Training1 (0%)

Frequent sub-topics

Automated research gap identification using embeddings and LLMs in systematic literature reviews · 1Multi-model embedding combinations for SLR screening · 1AI-driven research gap identification using embeddings and clustering · 1bibliometric analysis of AI in medical imaging research, generative and foundation-based methods · 1bibliometric analysis of human factors in maritime accidents · 1metadata and topic modeling analysis of circular economy literature · 1LLM-assisted data extraction and consensus statement generation · 1LLM-based PRISMA 2020 guideline adherence checking · 1

Open research gaps

  • The authors identify several gaps: (1) lack of standardized benchmarks for evaluating LLM-generated search strategies; (2) limited validation comparing LLM-generated SAPs with those from human biostat
  • Limited COS development in neurology despite success in other fields
  • Extension to full-text analysis beyond abstracts to capture methodological details and contextual nuances for improved classification accuracy on complex dimensions
  • Need for disease-specific COS guidance for trialists
  • Integration of domain-specific LLMs in RAG systems for SLRs

Representative papers

Related priority directions

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