Axelium vs LitFlow

Axelium is an AI-assisted platform for systematic reviews and meta-analyses, spanning evidence search, screening, analysis, reporting, and living reviews. Its 11 stages include protocol, search, screening, full-text review, extraction, RoB 2.0, GRADE, statistics, network meta-analysis, reporting, and living reviews. The maker says AI extracts structured values while deterministic code calculates statistics, and data points link to snippets in original PDFs. Listed methods include pairwise and network synthesis, Cochrane RoB 2.0, GRADE certainty ratings, CERQual, and PRISMA-style reporting. It imports references from tools and databases including EndNote, Zotero, PubMed, ClinicalTrials.gov, and Web of Science; exports include DOCX, XLSX, RIS, BibTeX, and RevMan. Team controls include roles, comments, dual-person sign-off, conflict adjudication, and versioned audit snapshots. A free plan includes one lifetime review and one seat; Researcher costs $29 a month. Human validation is needed for screening suggestions, extracted values, risk-of-bias assessments, and draft reports. Recurring statistical refresh for living-review network meta-analysis is paused, though scheduled cycles can import, screen, and extract new evidence.

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.