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Enter Your Research Topic
Start with a topic, question, or area of inquiry. You can also choose whether you want a standard systematic literature review or a meta-analysis. An optional year filter helps narrow the search scope when needed.
AI Systematic Review & Meta-Analysis Automation for Faster, More Rigorous Evidence Synthesis
Automate your systematic literature review (SLR) and meta-analysis workflow with Gatsbi Reviewer - from intelligent study screening and data extraction to bias assessment, statistical synthesis, and structured manuscript generation. Built for researchers, students, clinicians, and academic professionals who need rigorous outputs without the usual manual burden.
How It Works
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Let Gatsbi Search and Screen
Gatsbi Reviewer searches across major academic databases, screens relevant studies, and prepares a candidate list for your review.
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Review and Curate Included Studies
You can select or deselect studies and upload your own PDFs if you want to include papers not retrieved automatically.
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Run Synthesis or Generate the Manuscript
Choose the workflow path based on your review objective.
- For SLR, Gatsbi can move directly into synthesis and draft generation.
- For meta-analysis, the workflow enters a data extraction stage, then produces key statistical results and visual outputs before manuscript writing.
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Export and Finalize
You can export extracted raw data and key results, then continue refining the manuscript for submission, reporting, or internal use.
Turn Weeks of Review Work into a Structured First Draft
Systematic reviews and meta-analyses are often slowed down by repetitive manual tasks: searching, screening, extracting effect sizes, checking bias, generating plots, and writing up results. Gatsbi Reviewer brings these steps into one workflow so you can move from topic to evidence synthesis far more efficiently.
With Gatsbi Reviewer, you can:
- Search and screen relevant studies automatically
- Extract effect sizes, confidence intervals, sample sizes, and related quantitative data
- Run fixed-effect or random-effects meta-analysis
- Generate forest plots, funnel plots, and heterogeneity metrics such as I² and Q
- Apply Risk of Bias (RoB) or GRADE-like quality assessment
- Produce a structured draft with citations, methods, results, and figures
Browse sample SLR drafts generated by Gatsbi Reviewer.
Core Features
Smart Study Screening
Enter your research topic and let Gatsbi Reviewer automatically search across major academic databases, identify relevant papers, and screen candidate studies for inclusion. Instead of starting from a blank spreadsheet, you begin with an AI-assisted shortlist.
Best for:
- Systematic literature reviews
- Scoping reviews with structured evidence collection
- Rapid evidence synthesis for papers, grant proposals, or policy briefs
Automated Data Extraction
For eligible studies, Gatsbi Reviewer can extract key quantitative fields such as effect sizes, confidence intervals, sample sizes, and other structured data needed for downstream synthesis. This reduces one of the most time-consuming parts of meta-analysis preparation.
What this helps you do:
- Standardize extraction across multiple papers
- Reduce repetitive manual copy-paste work
- Move faster from paper selection to synthesis
Built-in Meta-Analysis Engine
Gatsbi Reviewer includes an integrated meta-analysis workflow that supports fixed-effect models, random-effects models, forest plots, funnel plots, heterogeneity metrics (I², Q), and publication bias assessment. This makes it easier to generate interpretable quantitative outputs without stitching together multiple external tools.
Bias and Quality Assessment
Assess included studies with RoB (Risk of Bias) and GRADE-like quality scoring, guided by AI suggestions to help structure your evaluation workflow.
Why it matters:
- Supports more transparent review methodology
- Helps strengthen the credibility of synthesis results
- Makes it easier to document review decisions consistently
Structured Manuscript Generation
Once your evidence base is ready, Gatsbi Reviewer can generate a structured review manuscript in minutes.
Depending on the workflow, the output can include:
- Abstract
- Introduction
- Methods
- Results
- Discussion
- Tables and figures
- Citations and references
Citation-Aware Writing
Gatsbi Reviewer is designed to preserve and format citations during writing, helping you maintain traceability between source evidence and the draft manuscript. The product page also highlights direct access to Google Scholar data for citation-aware workflows.
Why Researchers Choose Gatsbi Reviewer
End-to-End Review Workflow
From study identification to manuscript generation, Gatsbi Reviewer is designed to cover the full evidence synthesis pipeline in one place.
AI-Powered Study Screening
Automatically retrieve, rank, and screen relevant studies based on your research topic, reducing the time spent on manual triage.
Built-In Meta-Analysis Engine
Run quantitative synthesis without external coding workflows. Generate pooled results, forest plots, funnel plots, and heterogeneity statistics directly inside the platform.
Structured Manuscript Generation
Create a clear, publication-oriented draft with sections such as Introduction, Methods, Results, and Discussion, plus tables, plots, and references where applicable.
Methodological Rigor
Gatsbi Reviewer highlights PRISMA-oriented structure, standardized metrics, and reproducible outputs to support transparent academic reporting.
No Coding Required
Designed for users who want advanced evidence synthesis workflows without needing statistics software or programming experience.
What Makes Gatsbi Reviewer Different
Most tools help with only one part of the review process - search, screening, analysis, or writing. Gatsbi Reviewer is positioned as a more integrated workflow that connects:
topic input -> study screening -> data extraction -> meta-analysis -> manuscript generation
That means fewer tool switches, less manual coordination, and a faster path to a usable draft.
Who Gatsbi Reviewer Is Built For
Researchers Speed up evidence synthesis while maintaining methodological structure and traceable outputs.
Graduate Students and PhD Candidates Generate a stronger first draft for literature reviews and quantitative syntheses without building the full workflow manually.
Clinicians and Health Researchers Use structured screening, extraction, and synthesis workflows to accelerate review-based research projects.
Policy and Grant Writers Create evidence-backed review outputs for policy briefs, research proposals, and funding applications.
Use Cases
AI Systematic Literature Review
Generate a structured SLR draft from a research topic with automated study screening and thematic synthesis.
AI Meta-Analysis
Extract quantitative data, run pooled analysis, and generate results sections with plots and heterogeneity statistics.
Evidence Synthesis for Academic Writing
Use review outputs as a foundation for journal manuscripts, theses, dissertations, and conference papers.
Rapid Review Support
Accelerate the early stages of evidence collection and synthesis when timelines are tight.
Frequently Asked Questions
- Yes. The workflow supports both standard systematic literature reviews and meta-analyses, with a dedicated meta-analysis option during setup.
- Yes. The built-in meta-analysis engine includes forest plots, funnel plots, and heterogeneity metrics such as I² and Q.
- No. Gatsbi Reviewer is positioned as a no-coding workflow designed for researchers who want accessible review and synthesis tools.
- Yes. You can upload your own PDFs to include studies not retrieved automatically.
- Yes. Extracted raw data and key results for meta-analyses can be exported.