ASReview vs SWIFT-Review

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.

SWIFT-Review is a desktop workbench for formulating systematic reviews and prioritizing literature for screening. Statistical text mining and machine learning identify over-represented topics and rank documents for manual review. Users can search, categorize, prioritize, visualize, and extract information from large citation sets. The ranking workflow requires an initial training seed, and the software does not estimate how many relevant articles remain above a threshold. It can import PubMed IDs or XML, RIS files, and previously saved SWIFT Text-mining Project files; PMID imports retrieve citation data through NLM’s Entrez e-Utilities interface. Exports include Excel, RIS, and plain tab-delimited text. The free desktop software is available for Windows and Mac, with downloads through Sciome’s applications portal after signing in or creating a free account. Installation requires administrative access. The guide lists 64-bit Windows 7, 8, or 10 and 64-bit Mac OS X 10.8 or later, and recommends 8 GB RAM; very large projects may need more. Minor updates usually happen automatically, while major updates may require administrator privileges.