SciSpace Academy
Tutorial

AI-Assisted Rapid Reviews and Evidence Synthesis with SciSpace

Dr. Ali HindiEnglishSystematic review

TL;DR

1-min read

Systematic literature reviews show what work already exists, how strong the evidence is and where gaps remain, yet planning and running one can feel slow and complex. That is why many researchers turn to faster formats such as rapid reviews. This tutorial explains how to approach a review in a structured, methodical way with SciSpace so evidence synthesis speeds up without becoming haphazard. It is aimed at graduate students, PhD researchers, faculty and anyone starting or refining a literature review process.

Key takeaways

  • Systematic reviews matter because they identify the existing evidence, judge its strength and reveal research gaps.
  • A rapid review follows systematic principles but streamlines some steps, for example by limiting databases or date ranges, or dropping dual screening, so results arrive sooner.
  • Rapid reviews suit time-sensitive decisions, but their shortcuts should be reported openly so readers understand the possible limitations.
  • Organising the work from the planning stage onward keeps the review methodical, whichever format you choose.
  • AI can speed up the slowest parts of a review, including searching, screening titles and abstracts, extracting data into tables and summarising findings across studies.
  • Researchers should still check AI screening decisions, verify extracted data against the original papers and describe AI use in the methods section.
Apply it

To apply this, decide whether your next project needs a rapid or full systematic review, plan it up front, and use SciSpace for searching, screening and extraction with your own checks at each step.

How to do it

  1. Agree on the question, timeframe, stakeholders, and minimum viable evidence product.

    Agree on the question, timeframe, stakeholders, decision deadline, and minimum evidence product. State which elements of a full review will be streamlined and why.

  2. Write a brief protocol listing search sources, restrictions, criteria, and planned synthesis.

    Write a brief protocol covering sources, date or language restrictions, eligibility criteria, screening process, extraction fields, and synthesis. Preserve all deviations.

  3. Use SciSpace to search, deduplicate, prioritize, screen, and extract structured evidence.

    Use SciSpace to search, deduplicate, prioritize, screen, and extract evidence, but retain a log of queries, counts, decisions, and reasons. Do not hide prioritization rules.

  4. Validate a sample and all pivotal studies, then assess major risks of bias.

    Validate a sample and all pivotal studies against full texts and assess major risks of bias. Escalate uncertain inclusion or extraction decisions to a reviewer.

  5. Synthesize the evidence and clearly state what the rapid method may have missed.

    Synthesize the evidence with clear confidence and applicability statements. Report the restrictions and the kinds of studies or findings the rapid method may have missed.

Watch the tutorial49 min

AI-Assisted Rapid Reviews and Evidence Synthesis with SciSpacePlay video: AI-Assisted Rapid Reviews and Evidence Synthesis with SciSpace

Chapters

  1. Welcome, speaker intro and agenda
  2. What is a systematic literature review and why does it take 12-18 months?
  3. What is a rapid review vs a systematic review?
  4. When should you do a rapid review, and when not?
  5. Why AI is a natural fit for rapid reviews
  6. Boolean keyword search vs AI semantic search for systematic reviews
  7. How to check if a systematic review already exists on your topic
  8. How to start a rapid review in SciSpace following PRISMA
  9. How to turn a research question into PICO with AI
  10. How to set inclusion and exclusion criteria with AI
  11. How AI builds a search strategy across PubMed and Google Scholar
  12. Title and abstract screening with AI as a second reviewer
  13. How to review AI include and exclude decisions
  14. How to save included papers into a library folder
  15. How to verify AI exclusions with Chat with PDF
  16. How to build a data extraction table and export it to Excel
  17. Can AI generate a PRISMA flow diagram and review report?
  18. How to get a thematic analysis of review findings with AI
  19. AI-assisted rapid review workflow recap
  20. Does AI hallucinate citations? Quality appraisal with AI
  21. How to disclose AI use in your methods section
  22. What if AI misses a key paper in your review?
  23. Key takeaways on AI-assisted rapid reviews

Try this on SciSpace

Frequently asked questions

What is the difference between a rapid review and a systematic review?

A rapid review follows systematic review principles but streamlines or omits some steps, such as limiting databases, date ranges or dual screening, to deliver results faster. A full systematic review aims for comprehensive coverage and maximum rigor. Rapid reviews suit time-sensitive decisions, but their shortcuts should be reported transparently so readers understand possible limitations.

How can AI help with evidence synthesis?

AI can speed up searching, screening titles and abstracts, extracting data into tables and summarising findings across studies. These steps often take the most time in a review. Researchers should still check AI screening decisions, verify extracted data against the original papers and describe how AI was used in the methods section.

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