Your team needs a systematic review done in weeks, not the usual year-plus. The searches return thousands of records across PubMed, Scopus and OpenAlex. The duplicates pile up in Zotero, and reviewer disagreements stall in Rayyan. Meanwhile everyone assumes Elicit can “just do the screening” without anyone checking whether a journal or PROSPERO will accept AI-assisted methods. Guessing gets expensive. A shortcut you can’t document gets flagged at peer review. An extraction table with one invented value can undo months of work. And the credit bill can grow quietly while you chase a PRISMA flow diagram that doesn’t reconcile.
This guide is for graduate researchers, clinical and public health teams, librarians and evidence-synthesis consultants who already know what a systematic review is. You should be comfortable with basic Boolean searching, a reference manager and the idea of inclusion criteria. You don’t need any coding. It does not teach meta-analysis statistics, GRADE certainty ratings in depth, or how to write the discussion section. It also isn’t a substitute for your institution’s methods training or a statistician’s input.
An honest note on AI’s role. Elicit and its companion tools are strong at speeding up first-pass screening, finding candidate studies and drafting extraction tables. They are weak at subtle eligibility judgements, reading numbers from complex tables, and anything a paper doesn’t state outright. Tools like these can sound confident and still be wrong. Human judgement stays non-negotiable at the final inclusion decisions, data verification, risk-of-bias grading and anything that ends up in your reported methods. The guide shows where those checkpoints go and why.
What This Guide Covers
- A complete, connected workflow that takes you from research question to PRISMA 2020 diagram without switching tools blind.
- A clear map of the 2026 toolstack (Elicit, Zotero, Rayyan, ASReview, Consensus and Scite) so you know which tool fits each stage and which you can skip.
- A tighter question and a registration-ready protocol so later scope creep doesn’t damage your review.
- Search strategies that hold up across databases, built for recall and reproducibility in PubMed, Scopus and OpenAlex.
- A cleaner record set before screening starts, with fewer duplicates and a reliable count for your flow diagram.
- Faster title and abstract screening that uses AI without giving up the decisions reviewers will question.
- Defensible dual-reviewer and active-learning screening, including how to decide when it’s reasonable to stop.
- Structured extraction tables you can trust, with verification built in rather than bolted on.
- Stronger risk-of-bias and citation checks that catch retracted, contradicted or overstated sources early.
- A PRISMA 2020 flow diagram and audit trail that reconcile, so reviewers and editors can follow every number.
- Realistic cost, credit and time benchmarks against manual review, so you can budget and scope honestly.
- Straight answers on accuracy and acceptability: how to limit hallucination risk and how to report AI use in ways journals and PROSPERO recognise.
- Lessons from real-world cases that show the most common failure points before they reach your project.
- A look ahead at living reviews and agentic evidence synthesis, so the workflow you build now still works next year.
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