Uses active learning to prioritize articles for review based on your initial screening decisions, continuously improving relevance predictions.
Provides tools for assigning screening tasks to team members, tracking progress, and resolving conflicts with built-in consensus algorithms.
Automatically imports references from major databases and identifies duplicate records across uploaded files.
Allows users to design structured forms for extracting specific data points (e.g., PICO elements, outcomes) during full-text review.
Automatically generates PRISMA flow diagrams and detailed screening statistics to support manuscript preparation and methodological transparency.
PhD students or faculty conducting a systematic review for a dissertation or publication use SysRev to manage thousands of citations from databases like Scopus. They train the AI on initial relevance judgments, which then surfaces the most pertinent studies, allowing the researcher to complete the screening phase in weeks instead of months. The platform's collaboration features enable co-authors to divide the workload and reconcile disagreements efficiently, while the automated PRISMA diagram aids in manuscript submission.
Medical associations and guideline committees use SysRev to synthesize evidence for new clinical practice recommendations. Teams import studies from clinical trial registries and medical databases, screen for relevant RCTs and meta-analyses, and extract outcome data using custom forms. The audit trail and consensus tools are critical for maintaining methodological rigor and transparency, which is essential for guideline credibility and regulatory acceptance.
Drug safety teams in pharmaceutical companies employ SysRev to perform rapid reviews of adverse event literature for regulatory submissions to agencies like the FDA or EMA. They upload internal reports and public literature, using AI to identify relevant safety signals. The platform's data extraction and export capabilities streamline the creation of integrated safety summaries required for drug approval or post-marketing surveillance.
Undergraduate or graduate students learning research methods use the free tier of SysRev to conduct smaller-scale reviews or scoping reviews for course projects. The guided workflow helps them understand systematic review protocols, and the AI assistant provides a practical introduction to machine learning in research without requiring coding skills. This demystifies evidence synthesis and improves the quality of student research outputs.
R&D and business intelligence analysts in technology or biotech firms use SysRev to monitor scientific and patent literature for emerging trends and competitor activity. They set up ongoing projects that automatically ingest new publications, using AI to filter for relevance. The collaborative environment allows cross-functional teams to annotate and discuss findings, turning literature into actionable strategic insights.
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