ASReview Screening

Use active learning to prioritize and screen papers for systematic reviews, reducing manual workload by up to 95%. ASReview uses machine learning to learn from your screening decisions and prioritize the most likely relevant papers.

When to Use

• "I have 500 papers to screen for my systematic review"
• "Help me prioritize papers for inclusion"
• "Set up active learning screening for my review"
• "How many papers do I need to screen manually?"

When NOT to Use

• Searching for papers (use literature-search)
• Performing meta-analysis (use meta-analysis)
• Writing the review (use systematic-review + paper-writing)
• Small sets (< 50 papers), manual screening is faster

Setup

Install ASReview

pip install asreview asreview-insights asreview-datatools

Prepare Input Data

ASReview accepts RIS, CSV, TSV, or Excel files with at minimum:

• title: Paper title
• abstract: Paper abstract

Optional but recommended:

• doi, authors, year, keywords, label (if some are pre-labeled)

Export from Search Results

# Convert Semantic Scholar / OpenAlex results to ASReview format
import csv

def export_for_asreview(papers: list[dict], output_path: str):
    """Export papers to CSV for ASReview."""
    with open(output_path, 'w', newline='', encoding='utf-8') as f:
        writer = csv.DictWriter(f, fieldnames=[
            'title', 'abstract', 'authors', 'year', 'doi', 'keywords'
        ])
        writer.writeheader()
        for p in papers:
            writer.writerow({
                'title': p.get('title', ''),
                'abstract': p.get('abstract', ''),
                'authors': '; '.join(a.get('name', '') for a in p.get('authors', [])),
                'year': p.get('year', ''),
                'doi': p.get('externalIds', {}).get('DOI', ''),
                'keywords': '; '.join(p.get('fieldsOfStudy', []))
            })
    print(f"Exported {len(papers)} papers to {output_path}")