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Research Screener vs Universal SLR Assistant

Research Screener

researchscreener.com

Universal SLR Assistant

sadeghanisi.github.io

Side by side

FigureResearch ScreenerUniversal SLR Assistant
Price from—Free
Free plan—Yes
Free trial——
Open source—Yes
WebYesYes
Windows—Yes
macOS—Yes
Linux—Yes
API—Yes
Self-hosted—Yes
Plans published31
Spec score · AI Literature Review Software7.0 (#31)8.2 (#10)
ExportsExport options are provided for screening data and results.Workspace exports include decision CSV/XLSX files, AI and human decision logs, exclusion reasons, PRISMA-ready counts, a methods draft and a manifest.
PurposeResearch Screener uses machine learning to help find relevant research faster during systematic reviews.Universal SLR Assistant supports systematic and scoping review workflows with AI-assisted screening, extraction, and reproducible outputs.
SupportThe site lists Dr. Leo Ng and Dr. Kevin Chai as contact people and provides [email protected].The project directs users to the Complete User Guide, in-app Help panel, output logs, tests, and GitHub issues for support.

Plans

Research Screener

  • 40% discountA$150, paid once
  • 20% discountA$200, paid once
  • StandardA$250, paid once

Research Screener pricing →

Universal SLR Assistant

  • Free and open sourceFree

Universal SLR Assistant pricing →

In their own words

Research Screener

Research Screener has no write-up on Specifiction yet; its figures are above.

Visit researchscreener.com

Universal SLR Assistant

Universal SLR Assistant supports systematic and scoping reviews with AI-assisted screening, extraction, and reproducible outputs. It provides a desktop interface and a browser-based local Web App, and imports RIS, BibTeX, CSV, and plain-text references, including exports from PubMed, Scopus, and Web of Science. Its workflow covers abstract screening, full-text PDF processing, structured extraction, and export; likely duplicates are identified by DOI and fuzzy title matching. Workspace mode stores references, PDF metadata, review items, decisions, and audit events in a local SQLite workspace, separating AI suggestions from human decisions. Exports can include decision files and logs, exclusion reasons, PRISMA-ready counts, a methods disclosure draft, and a manifest. The project supports a range of cloud and local AI providers. Cloud providers receive submitted paper text, while Ollama or another local endpoint can keep it on the user's computer or local network. AI results are assistance only: people must verify them and make final review and reporting decisions. The software is free under the MIT License; cloud AI provider API fees may apply.

Visit sadeghanisi.github.io