ASReview vs Universal SLR Assistant
ASReview
asreview.nlUniversal SLR Assistant
sadeghanisi.github.ioSide by side
| Figure | ASReview | Universal SLR Assistant |
|---|---|---|
| Price from | Free | Free |
| Free plan | Yes | Yes |
| Free trial | — | — |
| Open source | Yes | Yes |
| Web | Yes | Yes |
| Windows | Yes | Yes |
| macOS | Yes | Yes |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 2 | 1 |
| Spec score · AI Literature Review Software | 9.0 (#1) | 8.2 (#10) |
| Spec score · Systematic Review Software | 9.1 (#1) | 8.2 (#6) |
| Human decisions | Human reviewers label records while the model continuously updates the ranking. | AI outputs remain suggestions, while final Include, Exclude or Maybe decisions require a human reviewer. |
| Intended users | The site presents ASReview for screeners, information specialists, data scientists, developers, institutions, and students. | It is intended for PhD students, researchers, professors, evidence synthesis teams, and systematic or scoping review authors. |
| Privacy | The site says the default local setup keeps data on the user's computer; server deployments keep data within the user's environment and apply that organization's policies. | The Web App binds to 127.0.0.1 for local use and is not intended as a public multi-user service. |
| Purpose | ASReview uses AI-aided active learning to prioritize records for systematic review screening while keeping a human screener in the decision loop. | Universal SLR Assistant supports systematic and scoping review workflows with AI-assisted screening, extraction, and reproducible outputs. |
| Support | The site offers free online documentation, tutorials, videos, and community discussion support, and describes in-house workshops and custom consultancy for organizations. | The project directs users to the Complete User Guide, in-app Help panel, output logs, tests, and GitHub issues for support. |
| What it does | ASReview uses active learning to reorder large collections of textual records so likely-relevant items appear earlier during systematic-review screening. | Universal SLR Assistant helps researchers import references, deduplicate records, screen literature, process PDFs, extract structured data and export auditable review outputs. |
Plans
In their own words
ASReview
ASReview is a free, open-source tool that uses AI-aided active learning to prioritize records for systematic review screening, with a human making screening decisions. Its site says the approach can reduce screening workload by up to 95%. Users can simulate AI-aided review to compare models, while Crowdscreen supports parallel screening with access managed by administrators through a private login system. The described model families are ELAS-Ultra, ELAS-Heavy, and language-agnostic ELAS-L; ELAS-Heavy and ELAS-L require the Dory package. ASReview works with reference managers including EndNote and PubMed, and imports and exports CSV, RIS, and XLSX files. Documentation includes a Python API, while the institutional workflow also describes a REST API and CLI for automation. In a local setup, data stays on the user's computer; server deployments keep it in the organization's environment and follow its policies. Support includes documentation, tutorials, videos, community discussion, and organizational workshops and consultancy. ASReview LAB costs nothing with no licensing fees or subscriptions, though running hardware or a server may incur costs.
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.