AI for Literature Search and Review: Possibilities and Ethics
TL;DR
1-min readThis tutorial is for public health professionals, researchers, educators and graduate students who want to speed up literature reviews responsibly. It is led by Paul Hassan Ilegbusi, Senior Lecturer and Deputy Director (Community Health) at Ondo State College of Health Technology, Akure, Nigeria, and Local Network Lead for the African Reproducibility Network (AREN). He balances what AI tools make possible with the ethical guardrails needed to keep reviews trustworthy.
Key takeaways
- SciSpace can streamline literature search and screening, shortening the time it takes to reach a relevant set of studies.
- The tutorial shows how to extract key insights from research papers efficiently and use them to support evidence-based synthesis.
- AI summaries can omit limitations, misstate effect sizes or miss context such as study population and design, so citing a paper on the basis of a summary alone risks misrepresenting it.
- A safer approach is to use summaries to decide what to read, then confirm key details in the full text before drawing conclusions.
- Reproducibility depends on documenting search terms, databases, dates, inclusion criteria, the tools used at each step and your screening decisions.
- Ethical guidelines covered in the tutorial keep transparency and human judgment at the centre of AI-assisted workflows.
To apply this in SciSpace, use its search and summaries for triage, verify each important finding in the original paper, and keep a record of searches and tools so others can follow your process.
How to do it
Search and screen with SciSpace
Run your literature search in SciSpace and screen the results to reach a relevant set of studies faster.
Use summaries to triage reading
Treat AI summaries as a guide to what to read, not as grounds for citing a paper. This is central to the ethical use of AI in literature review.
Confirm key details in the full text
Check limitations, effect sizes, study population and design in the full text, since summaries can omit or misstate them.
Extract insights for evidence synthesis
Extract key insights from the papers you have verified and use them to support evidence-based synthesis.
Document your search process
Record search terms, databases, dates, inclusion criteria, the tools used at each step and your screening decisions, so others can follow and reproduce your process.
Try this on SciSpace
Literature Review Generator
Open agent →Plagiarism Checker
Open agent →Literature Review Matrix
Open agent →
Frequently asked questions
Why is relying on AI summaries risky in a literature review?
Summaries can omit limitations, misstate effect sizes or miss context such as study population and design. If you cite a paper based only on a summary, you may misrepresent it. Use summaries to decide what to read, then check key details in the full text before drawing conclusions or citing.
What makes an AI-assisted literature review reproducible?
Document your search terms, databases, dates, inclusion criteria and the tools used at each step, and keep records of screening decisions. Verify AI outputs against original papers. With this information, another researcher can follow your process, understand the role AI played and assess whether your conclusions hold.
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