Conduct Systematic Reviews with AI-Powered Mind Maps
Use AI mind maps to plan systematic reviews, define inclusion criteria, and synthesize findings across studies. Branch into search strategies and evidence quality.
Start from a template
Clone this PRISMA skeleton and fill in your own review question to plan every stage in one map.
How it works
Systematic reviews are the most structured form of research — and the most punishing when you miss a step. PRISMA guidelines, risk-of-bias assessment, heterogeneity analysis, and sensitivity checks must all be planned before the first search. Mind maps turn this checklist into a navigable structure.
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Define your review question. Enter your research question using the PICO framework (Population, Intervention, Comparator, Outcome). The AI maps the major components of a systematic review protocol aligned with PRISMA guidelines.
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Branch into search strategy. Expand into databases, search terms, Boolean operators, and date restrictions. Each database branch includes its specific controlled vocabulary — MeSH terms for PubMed, Thesaurus terms for PsycINFO. The map ensures no source is overlooked.
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Map the screening pipeline. Branch into title-abstract screening criteria, full-text inclusion criteria, and data extraction variables. For each criterion, specify how borderline cases will be handled. The visual layout shows the complete decision tree a screener must follow.
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Plan the synthesis. Branch into qualitative synthesis (thematic grouping of findings), quantitative synthesis (meta-analysis if appropriate), and sensitivity analyses. Under meta-analysis, map your effect size choice, heterogeneity assessment, and subgroup analyses.
Why branching matters for systematic reviews
Systematic reviews are inherently hierarchical and branching. Your search strategy branches by database. Your screening branches at each decision point. Your synthesis branches by outcome and subgroup. A linear protocol document obscures this structure — you end up flipping between sections to understand how a screening decision affects your synthesis plan.
Mind maps also help with the collaborative nature of reviews. When two reviewers need to calibrate on screening criteria, a visual decision tree is more effective than paragraphs of inclusion rules. Each branch of the tree can be discussed, refined, and agreed upon independently. Disagreements become visible as specific branch points rather than vague differences in interpretation.
Example
You’re conducting a systematic review of exercise interventions for depression in adolescents. Branching into study designs, you include RCTs and controlled trials but debate whether to include pre-post studies without a control group. You create branches for both inclusion and exclusion scenarios and discover that including pre-post studies nearly triples your eligible papers but introduces substantial bias risk. You decide to include them in a secondary sensitivity analysis while keeping the primary analysis restricted to controlled designs — a decision documented right in the map.
Pair this workflow with citation mapping to find studies through reference chaining, or use data analysis planning to detail your meta-analytic approach. A literature survey can help scope the field before committing to a full systematic review protocol.