Smart Data Extraction
Extract structured data from research papers using advanced AI that reads, understands, and populates your forms automatically.
How It Works
Intelligent PDF Processing
- Document Analysis: AI reads the entire paper structure
- Content Recognition: Identifies sections, tables, figures
- Data Identification: Locates relevant information
- Field Population: Pre-fills your extraction form
- Source Linking: Shows exactly where data came from
Confidence Indicators
Every extracted field shows:
- 🟢 High confidence: AI is certain (verify quickly)
- 🟡 Medium confidence: Check recommended
- 🔴 Low confidence: Manual entry needed
- ⚪ Not found: Data not detected in paper
Extraction Forms
Pre-Built Templates
Choose from validated templates:
- RCT Template: Randomized controlled trials
- Cohort Study Template: Observational research
- Diagnostic Accuracy: Test evaluation studies
- Qualitative Studies: Thematic data extraction
Custom Forms
Create forms tailored to your review:
- Drag-and-drop field builder
- Multiple field types (text, number, dropdown, etc.)
- Conditional logic
- Repeating groups (multiple arms, outcomes)
Data Categories
Study Characteristics
- Publication details
- Study design
- Country/setting
- Funding sources
- Registration information
Population
- Sample size (total, per group)
- Demographics (age, sex)
- Inclusion/exclusion criteria
- Baseline characteristics
Intervention
- Description
- Dosage/intensity
- Duration
- Delivery method
- Provider
Comparators
- Control type
- Description
- Matching details
Outcomes
- Definition
- Measurement tool
- Timing
- Results (means, SDs, events)
Working with Extracted Data
Verification Workflow
- Review pre-filled data
- Click to see source in original PDF
- Confirm or correct each field
- Add notes for clarification
Dual Extraction
For critical data:
- Two extractors work independently
- Discrepancies highlighted
- Reconciliation interface
- Audit trail maintained
Data Validation
Automatic checks for:
- Numerical consistency
- Required field completion
- Cross-field logic
- Statistical plausibility
Table Extraction
Automatic Table Detection
AI identifies and parses:
- Results tables
- Baseline characteristics
- Outcome summaries
- Statistical analyses
Table-to-Form Mapping
- Select table cells
- Map to extraction fields
- Import multiple rows at once
Export Options
Structured Formats
- Excel/CSV with all extracted data
- RevMan-compatible format
- PRISMA data files
For Analysis
- Ready for meta-analysis software
- Statistical package compatible
- R/Stata friendly formats
Best Practices
For Quality
- Always verify AI-extracted numerical data
- Cross-check statistical results
- Document unclear/missing data
- Use "not reported" consistently
For Efficiency
- Extract in batches by study design
- Use pre-filled data as starting point
- Focus manual effort on complex outcomes
- Build reusable custom templates