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AI-enabled literature review automation and enterprise evidence management software.

DistillerSR is an AI-powered platform designed to streamline and automate the entire literature review lifecycle. It supports scalable projects with over 675,000 references, providing a unified environment for managing and tracking every stage of the review process. The platform employs AI to accelerate screening, data extraction, and synthesis. Its configurable workflows support various research protocols and team configurations. Key features include automated reference importing, deduplication, and relevance ranking using machine learning algorithms. DistillerSR's architecture ensures data integrity and audit readiness, tracking every search and change made throughout the review. It integrates with various data sources and supports collaborative workflows, enabling efficient evidence-based decision-making for health policy, clinical practice guidelines, and regulatory submissions.
DistillerSR is an AI-powered platform designed to streamline and automate the entire literature review lifecycle.
Explore all tools that specialize in evidence synthesis. This domain focus ensures DistillerSR delivers optimized results for this specific requirement.
Utilizes machine learning to rank references by relevance, reducing screening time by up to 85%. Algorithm trained on user feedback to improve accuracy.
Automatically extracts data from full-text articles using pre-defined templates and natural language processing (NLP).
Supports fully customizable review workflows, allowing users to tailor the process to their specific research protocols.
Tracks 100% of all searches, references, changes, and data points, ensuring audit readiness and regulatory compliance (e.g., GDPR, SOC2).
Provides tools for team collaboration, including user roles, permissions, and real-time communication features.
Account Setup & Configuration - Define user roles, permissions, and security settings.
Project Creation - Configure project-specific workflows, data sources, and review protocols.
Data Import - Import references from various databases (PubMed, Embase, Cochrane Library) or file formats (RIS, CSV).
AI Training - Train the AI on a subset of references to improve relevance ranking and screening accuracy.
Workflow Customization - Customize the review workflow, including screening criteria, data extraction forms, and synthesis methods.
Team Collaboration - Invite team members, assign roles, and manage access to projects and data.
Reporting & Analysis - Generate reports, data tables, and evidence summaries to support decision-making.
All Set
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Verified feedback from other users.
"Highly regarded for its AI-driven automation and audit-ready capabilities, enhancing the speed and accuracy of literature reviews."
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