ASReview vs LitFlow

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.

LitFlow is an AI-augmented workspace for systematic literature reviews, covering search, screening, extraction, synthesis, and reporting. Search includes database queries, snowballing, and AI-generated synonyms. For each record, AI proposes per-criterion verdicts with confidence scores, explanations, and links to source passages, while researchers make the decisions. Custom extraction schemas can receive LLM pre-fill with source links, and a qualitative coding board supports thematic structure. Reports include PRISMA information, Cohen’s κ, and an audit trail in a publication-ready methods report. AI suggestions and human decisions are logged with timestamps, actors, and rationales. Users can bring keys for Gemini, Claude, OpenAI, or Mistral, or use open-source models hosted by the University of Münster. The closed alpha runs on University of Münster infrastructure in the EU under GDPR, with pseudonymised personal data in research outputs. Operated by the university’s LitFlow research project in Münster, it is free for academic use, requires maintainer approval, and has no commercial tier listed.