SciSpace Academy
Tutorial

How to Conduct a Systematic Literature Review Using SciSpace

Rohan TondulkarEnglishSystematic review

TL;DR

1-min read

This live walkthrough is for researchers and students preparing a systematic review or meta-analysis who want a workflow that is both transparent and efficient. It is presented by Rohan Tondulkar, Research Scientist at SciSpace, who demonstrates guided how to conduct a systematic literature review on the platform. The tutorial combines established methodological practice with AI-powered tools and closes with interactive questions and answers.

Key takeaways

  • A systematic review identifies, appraises and synthesises all relevant studies on a defined question using a documented protocol, while a meta-analysis is a statistical technique that pools quantitative results from comparable studies.
  • Not every systematic review includes a meta-analysis, but the workflow shown applies to both types of project.
  • The demonstration follows each stage of the review in SciSpace, aiming to cut repetitive work without giving up academic rigor.
  • Every stage is kept traceable, so the review remains understandable to examiners, peer reviewers and journal editors.
  • Transparency comes from writing or registering a protocol, documenting search strings and databases, and logging each screening decision with its reason.
  • Noting which steps used AI tools and how their outputs were checked, and reporting this, typically following PRISMA, lets readers evaluate and reproduce the review.
Apply it

To apply this, set up your protocol first, then follow the walkthrough in SciSpace stage by stage while logging searches, screening decisions and AI-assisted steps as you go.

How to do it

  1. Follow a guided review workflow

    Work through a workflow in SciSpace to learn how to conduct a systematic literature review. A clear sequence keeps a long project manageable.

  2. Combine methodology with AI tools

    Pair established review methodology with SciSpace's AI-powered tools at each stage. This keeps the review rigorous while reducing manual effort.

  3. Keep each stage traceable

    Record your decisions at every stage so the review stays transparent and traceable. Others should be able to follow how you reached your results.

  4. Apply the workflow to meta-analyses

    Use the same workflow whether you are preparing a systematic review or a meta-analysis. One consistent process saves time across project types.

  5. Reduce repetitive work carefully

    Let AI handle repetitive tasks while you retain responsibility for academic rigour. This balance makes the review both efficient and trustworthy.

Watch the tutorial35 min

Systematic Literature Review with SciSpace: A Live WalkthroughPlay video: Systematic Literature Review with SciSpace: A Live Walkthrough

Chapters

  1. How to start a systematic literature review in SciSpace Agent
  2. PICO research questions for a systematic review with AI
  3. How to set inclusion and exclusion criteria with AI
  4. Systematic review search strategy: paper coverage vs credits
  5. Boolean search queries for PubMed and Google Scholar
  6. Is an AI systematic review reproducible?
  7. How to get more papers from PubMed and Google Scholar
  8. Relevance ranking and duplicate removal across databases
  9. Title and abstract screening on a 3,000-paper review
  10. How AI screening scores work: per-criterion reasoning and cut-offs
  11. Filtering included papers and exporting each screening step
  12. How full-text screening works in a systematic review
  13. How to extract data for a systematic review with AI
  14. PRISMA flow diagram generated automatically
  15. Does an AI systematic review hallucinate references?
  16. What's in the AI-generated PRISMA systematic review report
  17. How to prompt SciSpace Agent for a better systematic review
  18. How long does an AI systematic literature review take?
  19. Closing and handover to Q&A

Try this on SciSpace

Frequently asked questions

What is the difference between a systematic review and a meta-analysis?

A systematic review identifies, appraises and synthesises all relevant studies on a defined question using a documented protocol. A meta-analysis is a statistical technique, often used within a systematic review, that pools quantitative results from comparable studies to produce an overall estimate. Not every systematic review includes a meta-analysis.

How do I keep an AI-assisted systematic review transparent?

Register or write a protocol, document search strings and databases, record each screening decision with reasons, and note which steps used AI tools and how outputs were checked. Reporting this information, typically following PRISMA, allows peer reviewers and readers to evaluate and reproduce your review.

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