slr-automation-guide

name	slr-automation-guide
description	Tools and pipelines for automating systematic literature reviews
metadata	{"openclaw":{"emoji":"📋","category":"research","subcategory":"methodology","keywords":["systematic review","SLR","automation","screening","PRISMA","evidence synthesis"],"source":"https://github.com/asreview/asreview"}}

Systematic Literature Review Automation Guide

Overview

Systematic Literature Reviews (SLRs) are rigorous, reproducible surveys of research evidence following protocols like PRISMA and Cochrane. This guide covers tools that automate the most time-consuming steps — deduplication, title/abstract screening, full-text assessment, and data extraction — using active learning, NLP, and AI agents. Key tools include ASReview, Rayyan, and custom pipelines.

SLR Pipeline

Protocol Definition (PICO, inclusion/exclusion criteria)
         ↓
   Database Search (PubMed, Scopus, Web of Science)
         ↓
   Deduplication (ASReview, Rayyan, or custom)
         ↓
   Title/Abstract Screening (AI-assisted prioritization)
         ↓
   Full-text Assessment (relevance + quality)
         ↓
   Data Extraction (structured tables)
         ↓
   Quality Assessment (risk of bias)
         ↓
   Synthesis + PRISMA Report

ASReview (Active Learning)

# Install ASReview
pip install asreview

# Launch web interface
asreview lab

# CLI screening
asreview simulate benchmark:van_de_Schoot_2017 \
  -m nb -e tfidf \
  --n_prior_included 5 --n_prior_excluded 5 \
  -o results/simulation.asreview

Python API

import asreview
 asreview  ASReviewData, ReviewSimulate


data = ASReviewData.from_file()
()


sim = ReviewSimulate(
    data,
    model=,              
    feature_extraction=,
    query_strategy=,     
    n_prior_included=,
    n_prior_excluded=,
)
sim.review()


()