Rayyan is an AI-assisted systematic review platform that accelerates screening, extraction and collaboration for evidence synthesis.
Rayyan is built for the most tedious part of systematic reviews: screening thousands of titles and abstracts for inclusion. Its machine learning ranks records by relevance so reviewers tackle the likely includes first, and blind dual-screening workflows keep the methodology sound. AI features help with extraction, conflict resolution and detecting duplicates across databases. Millions of researchers — especially in health and social sciences — use it to move reviews from months to weeks.
Rayyan is freemium with a free tier for individual reviews; paid plans add advanced AI features, unlimited reviewers and organization controls.
Academic teams, guideline developers and health researchers conducting systematic reviews and meta-analyses.
Health research teams screen thousands of citations for systematic reviews, using AI ranking to prioritize likely includes. Evidence synthesis groups collaborate across institutions with blinded screening and conflict resolution.
Create a review, import citations from your reference manager and let the AI rank them before beginning dual screening.
Yes — a functional free tier exists; advanced AI and team features are paid.
Rayyan supports integrations and data exchange; broad API access is enterprise-level.
Versus Covidence, Rayyan is more affordable with comparable screening rigor; versus manual screening it dramatically reduces time while preserving methodology.
It trains on your include/exclude decisions as you screen, so prioritization improves with every judgment you make.
Yes, screening decisions, reasons and extractions export for PRISMA flows and downstream meta-analysis software.
Reviewers make inclusion decisions independently without seeing each other’s votes, and conflicts are resolved after — preserving the methodological rigor reviews demand.
Citation exports from PubMed, Zotero, EndNote and other reference managers import directly, along with RIS, BibTeX and CSV formats.
Yes — faster review types like scoping and rapid reviews benefit from the same screening acceleration even when full dual-screening rigor is not required.
Machine learning trained on your own inclusion decisions drives the prioritization, improving continuously as the review progresses.
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