MetaScreener is an open-source AI-assisted tool designed to streamline the screening process in systematic literature reviews and academic research workflows. The system helps researchers analyze large collections of academic abstracts and research papers to determine which studies are relevant for inclusion in evidence synthesis projects. Instead of manually reviewing hundreds or thousands of documents, researchers can use MetaScreener to apply machine learning techniques that assist with classification and prioritization of candidate papers. The platform can analyze both abstracts and full PDF documents, enabling automated filtering based on research criteria defined by the user. By incorporating natural language processing techniques, the system can identify potentially relevant studies and reduce the workload associated with manual screening.
Features
- AI-assisted screening of research abstracts and PDF documents
- Support for systematic review workflows and evidence synthesis projects
- Automated prioritization of relevant studies based on textual analysis
- Natural language processing for document classification and filtering
- Tools for managing large collections of academic papers
- Workflow support for accelerating literature review processes