How to Do a Systematic Literature Review with AI (2026)
What Makes a Review "Systematic"
A systematic literature review requires:
- A pre-registered protocol — your search strategy, inclusion/exclusion criteria, and analysis plan documented before you start
- Comprehensive searching — multiple databases searched using validated search strings
- Transparent screening — every paper assessed against explicit criteria, with reasons for exclusion recorded
- Data extraction — structured information pulled from each included study
- Quality assessment — each included study evaluated for methodological rigor
- Synthesis — findings combined in a way that answers your research question
AI can help with stages 1, 2, 3, 4, and 6. Stage 5 (quality assessment) requires your judgment, though AI can assist.
Stage 1: Protocol Development
Developing your PICO/PECO framework
Most systematic reviews in health and social sciences use PICO (Population, Intervention, Comparison, Outcome) or PECO (Population, Exposure, Comparator, Outcome) to structure the research question.
I'm planning a systematic review on [topic].
My research question is: [question]
Help me structure this using the PICO framework:
- Population: who are the participants?
- Intervention/Exposure: what is being studied?
- Comparison: what is it being compared to?
- Outcome: what outcomes are being measured?
Then help me refine my research question to be specific enough
for a systematic review.
Writing inclusion and exclusion criteria
My systematic review research question is: [question]
My PICO framework is: [PICO]
Draft inclusion and exclusion criteria covering:
1. Study design (which study types will I include?)
2. Population characteristics
3. Intervention/exposure specifics
4. Outcome measures
5. Publication date range
6. Language restrictions
7. Publication status (peer-reviewed only?)
For each criterion, explain the rationale — why is this included
or excluded?
Building your search string
My systematic review covers: [topic]
Key concepts: [list your main concepts]
PICO: [your PICO]
Build a comprehensive Boolean search string for [PubMed / Scopus / Web of Science].
Include:
- MeSH terms where relevant
- Free text synonyms for each concept
- Truncation symbols for variant spellings
- Boolean operators (AND/OR/NOT) connecting concepts
Also suggest 3-5 additional databases I should search beyond the primary one.
Stage 2: Database Searching
AI cannot search academic databases for you — this must be done directly in each database to ensure completeness and reproducibility. However, AI helps you build and validate your search strategy.
Databases to search for most systematic reviews:
- PubMed/MEDLINE (health sciences)
- Embase (pharmacology, clinical medicine)
- PsycINFO (psychology, behavioral science)
- Scopus (broad academic coverage)
- Web of Science (citation tracking)
- CINAHL (nursing, allied health)
- Cochrane Library (for health intervention reviews)
- Grey literature: ClinicalTrials.gov, WHO ICTRP, relevant government databases
After running searches in each database:
I ran searches in [databases] and retrieved [X] records total.
After deduplication I have [Y] records.
My inclusion criteria are: [criteria]
My exclusion criteria are: [criteria]
Can you help me create a PRISMA flow diagram description and
draft the search strategy section of my methods?
Stage 3: Screening
Screening is the most labor-intensive stage — reading thousands of titles and abstracts to determine which papers meet your inclusion criteria. AI can help, but with important caveats.
Title and abstract screening with AI
Important: AI screening should be validated against human screening before relying on it. For a published systematic review, AI screening alone is not yet accepted as methodologically sufficient — you must document your approach.
For initial triage:
I am screening papers for a systematic review on [topic].
Inclusion criteria:
[list your criteria]
Exclusion criteria:
[list your criteria]
For each title and abstract I paste, tell me:
- Include / Exclude / Uncertain
- Reason for decision (which criterion is met or not met)
Here are the first 10:
[paste titles and abstracts]
Work in batches of 10-20. For uncertain papers, always read the full text.
Calibration check:
Screen 50 papers with AI and also screen them yourself independently. Compare results. If agreement is above 90%, your criteria are well-defined. If below 80%, your criteria need clarification before continuing.
Full-text screening
Papers that pass title/abstract screening require full-text review. Upload each paper to Prismer or use ChatGPT to assess against your criteria:
Here is the full text of a paper I'm screening for my systematic review.
My inclusion criteria: [criteria]
My exclusion criteria: [criteria]
Based on the full text:
1. Does this paper meet all inclusion criteria?
2. Does it trigger any exclusion criteria?
3. Your recommendation: Include / Exclude / Contact authors for clarification
4. Rationale for your decision
Paper:
[paste full text or upload PDF]
Stage 4: Data Extraction
Data extraction means pulling standardized information from each included study into a structured form. AI dramatically speeds this up.
Creating your extraction form
My systematic review research question: [question]
I am reviewing [type of studies — RCTs / cohort studies / qualitative / mixed]
Create a data extraction form covering:
1. Study identification (author, year, country, journal)
2. Study design and methodology
3. Population characteristics (sample size, demographics, setting)
4. Intervention/exposure details
5. Comparison/control details
6. Outcome measures and results
7. Follow-up period
8. Funding source and conflicts of interest
9. Risk of bias indicators specific to this study type
Extracting data from papers
For each included paper:
Using this data extraction form: [paste your form]
Extract all relevant information from this paper.
For any fields where information is unclear or missing, note "NR" (not reported)
and flag it — I may need to contact the authors.
If you're uncertain about any extraction, mark it with [?] and explain why.
Paper:
[paste paper or key sections]
Critical rule: Always verify AI extraction against the original paper for key results. Extraction errors in systematic reviews have serious consequences — they propagate to meta-analyses and clinical guidelines.
Stage 5: Quality Assessment
Quality assessment (also called risk of bias assessment) evaluates the methodological rigor of each included study. This stage requires your judgment — AI can help you apply established tools but cannot replace your assessment.
Common quality assessment tools:
- RCTs: Cochrane Risk of Bias Tool (RoB 2)
- Observational studies: Newcastle-Ottawa Scale
- Qualitative studies: CASP Qualitative Checklist
- Diagnostic accuracy: QUADAS-2
Using AI to apply quality tools:
I am using the [RoB 2 / Newcastle-Ottawa Scale / CASP] to assess this study.
For each domain of the tool, help me identify the relevant information
from the paper and suggest a rating. Flag any domains where the paper
doesn't provide enough information to make a judgment.
I will make the final rating — I need you to help me locate the evidence.
Paper:
[paste paper]
Stage 6: Synthesis
Narrative synthesis
For reviews where studies are too heterogeneous for meta-analysis:
Here are my data extraction summaries from [X] included studies.
My research question: [question]
Synthesize the findings:
1. What is the overall direction of evidence? (consistent / inconsistent / mixed)
2. What explains differences in findings across studies?
3. What subgroups or moderators affect the findings?
4. What is the strength of evidence overall?
5. What gaps remain unanswered?
Summaries:
[paste your extraction summaries]
Using NotebookLM for synthesis
Upload all your included papers and extracted data to NotebookLM:
I've uploaded [X] papers included in my systematic review on [topic].
My research question: [question]
Across all included studies:
1. What outcome measures were used and how consistent are they?
2. What are the main findings grouped by outcome?
3. Where do studies agree and where do they contradict each other?
4. Which studies had the highest methodological quality (based on my quality ratings)?
5. Draft a narrative synthesis paragraph for [specific outcome]
Writing up the results
I need to write the results section of my systematic review.
My included studies: [X] papers
My main findings: [paste your synthesis]
Draft a results section that:
1. Describes the included studies (design, population, setting)
2. Presents findings organized by outcome
3. Notes where high-quality evidence exists vs. where evidence is weak
4. Uses appropriate hedging language for uncertain findings
5. Does not draw conclusions — that goes in the discussion
PRISMA Reporting
Systematic reviews are reported according to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines.
Help me draft the following PRISMA sections for my systematic review:
Search strategy:
- Databases: [list]
- Date of search: [date]
- Total records retrieved: [number]
- After deduplication: [number]
Screening:
- Records excluded at title/abstract: [number] (reason: [criteria])
- Full texts assessed: [number]
- Full texts excluded: [number] (reasons: [list with numbers])
- Studies included: [number]
Draft these sections in PRISMA-compliant language.
Tools for Systematic Reviews
| Stage | Tool | Cost |
|---|---|---|
| Protocol development | ChatGPT / Claude | Free |
| Search string building | ChatGPT / Claude | Free |
| Screening management | Rayyan, Covidence | Free / paid |
| Full-text screening | Prismer, ChatGPT | From $9.90/month |
| Data extraction | ChatGPT / Claude | Free |
| Quality assessment | ChatGPT (apply tools) | Free |
| Synthesis | NotebookLM | Free |
| Citation management | Zotero | Free |
Rayyan (rayyan.ai) is specifically designed for systematic review screening — it manages the workflow, tracks screening decisions, and supports dual-reviewer processes. Free tier is sufficient for most reviews.
Covidence is the gold standard systematic review platform used by Cochrane, but is expensive (~$500+/year). Most universities have institutional access — check yours.
Frequently Asked Questions
Can AI replace human reviewers in a systematic review? Not currently. Most journals and Cochrane require dual-reviewer screening and extraction for published systematic reviews. AI can assist one reviewer in working more efficiently, but the methodological standard of dual-reviewer review with consensus for disagreements still applies.
How long does a systematic review take with AI assistance? A well-scoped review with 50-200 included studies typically takes 3-6 months without AI. With AI assistance for screening and extraction, expect to save 30-50% of the time in those stages. Protocol development and write-up timelines are less affected.
What's the difference between a systematic review and a scoping review? A scoping review maps the extent of literature on a topic without the strict quality assessment and synthesis protocol of a systematic review. Scoping reviews are faster and appropriate when the field is broad or the question is exploratory. Systematic reviews are appropriate when you're trying to answer a specific clinical or policy question with quantifiable evidence.
Do I need to register my systematic review protocol? For health sciences systematic reviews, pre-registration in PROSPERO is strongly recommended and often required by journals. For other fields, the Open Science Framework (OSF) accepts systematic review pre-registrations. Registration protects against publication bias and outcome switching.
Can I do a systematic review alone? Single-author systematic reviews are published, but dual-reviewer screening and extraction is the methodological standard. If working alone, document your process carefully and consider having a colleague check a random sample of your screening decisions.
Processing dense academic papers for your systematic review? Try Prismer — upload any PDF and get structured notes and a comprehension quiz in 60 seconds. Plans from $9.90/month.