Searching for AI tools for a literature review usually turns up two different families of product, and mixing them up wastes setup time. General discovery and synthesis tools (Semantic Scholar, SciSpace, citation-mapping tools like Connected Papers) are built for the open-ended reading a PhD student or narrative-review author does across a body of work – see AI literature review tools for PhD students for that landscape. This guide covers the other family: tools built specifically around the formal systematic review pipeline – deduplicated search import, dual independent screening, structured data extraction, and risk-of-bias appraisal – the stages a PRISMA 2020-compliant review has to document and report.
Where these tools sit in the PRISMA workflow
PRISMA 2020 doesn’t mandate any specific software, but its reporting checklist and flow diagram assume a documented, reproducible sequence: search multiple databases plus grey literature, deduplicate records, screen titles/abstracts against eligibility criteria, screen full texts, extract data into a structured form, and appraise risk of bias or methodological quality in the included studies. The tools below cluster around these stages rather than replacing the review protocol itself – a review still needs a pre-specified question, eligibility criteria, and (for health-related topics) ideally a registered protocol before any tool gets used.
Screening tools: Rayyan, Covidence, and ASReview
Rayyan is a widely used, free-to-start screening platform built for exactly this stage: it imports records from reference managers or database exports, flags duplicates, and uses a machine-learning relevance ranking that improves as reviewers mark records included or excluded, so more-likely-relevant records surface earlier in a large batch. It supports blinded, independent screening by multiple reviewers with conflict resolution, which is the dual-screening approach PRISMA and Cochrane methodology expect for a rigorous review, and can generate a PRISMA flow diagram from the screening record.
Covidence is a fuller review workbench rather than a screening-only tool – it covers import/deduplication, title/abstract and full-text screening, data extraction forms, and risk-of-bias assessment in one connected workflow, and is the tool Cochrane recommends for authors preparing new Cochrane reviews. It’s a paid, per-review-team product; check current pricing directly, as this changes.
ASReview takes a different approach: it’s a free, open-source active-learning tool built by Utrecht University’s AI-aided Knowledge Discovery lab, first described by van de Schoot et al. in Nature Machine Intelligence (2021) and now on its LAB v2 release. Instead of ranking a fixed list once, it retrains its relevance model after every screening decision you make, aiming to surface the most-likely-relevant remaining records as early as possible in a large candidate set – the project has reported very large workload reductions in large-scale screening tasks, though the size of that reduction depends heavily on how rare relevant records are in a given search and shouldn’t be treated as a fixed, universal percentage for every review.
Data extraction: structured forms over free-text notes
Covidence and DistillerSR both build data extraction around configurable structured forms rather than free-text notes, which matters for two reasons: it forces reviewers to extract the same fields consistently across every included study, and it produces a table that can go straight into the results section or a Cochrane-style Summary of Findings table. DistillerSR in particular is positioned toward enterprise and regulated review work – pharmaceutical and medical-device teams producing systematic reviews for regulatory submissions, where audit trails and validated workflows matter as much as speed.
Elicit, covered in more depth in the PhD-focused AI literature review guide, sits partway between the discovery tools and this category: it searches a broad paper index rather than a fixed set of database exports, but its structured extraction tables and PRISMA 2020-aligned screening features make it a genuine option for the screening and extraction stages of a smaller systematic or scoping review, particularly one without institutional access to Covidence or DistillerSR.
Risk of bias and quality appraisal
Automating risk-of-bias assessment is the least mature part of this stack. Covidence and DistillerSR both provide structured templates for standard tools – the Cochrane risk-of-bias tool (RoB 2) for randomized trials, ROBINS-I for non-randomized studies – but the judgment itself is still a human reviewer task under current Cochrane Handbook guidance. Machine-assisted risk-of-bias tools exist in the research literature, but treat any tool claiming to fully automate this stage with the same caution PRISMA and Cochrane methodology apply generally: a reviewer’s documented judgment, not a model’s output, is what the reporting standard expects to see.
AI screening and extraction still need disclosure and human oversight
A joint position statement from Cochrane, the Campbell Collaboration, JBI, and the Collaboration for Environmental Evidence (published 31 October 2025, endorsing the RAISE recommendations for responsible AI use in evidence synthesis) sets out the expectation that now applies across this entire tool category: AI and automation should support, not replace, reviewer judgment; any AI use that makes or suggests eligibility, extraction, or risk-of-bias judgements needs disclosure, including the system name, version, and its role; and authors need to describe how AI-generated outputs were verified. Routine use for basic tasks like spelling or formatting generally falls outside that disclosure requirement – it’s judgment-affecting AI use specifically that needs to be reported in the methods section.
Choosing between these tools and the broader discovery/synthesis tools
If your project is a formal, PRISMA-reportable systematic or scoping review – especially one with a registered protocol, a defined search strategy across multiple databases, and a requirement to document screening decisions – the tools on this page (Rayyan, Covidence, ASReview, DistillerSR) are built around that exact accountability trail. If you’re doing a narrative or exploratory literature review without that formal reporting requirement, the broader discovery and citation-mapping tools covered in AI literature review tools for PhD students – including SciSpace for natural-language querying across papers – are usually the better fit. See literature review vs. systematic review vs. scoping review if you’re not yet sure which review type your project actually is, and the wider AI writing tools hub for the full landscape of AI-assisted research and writing tools beyond this specific workflow.
Frequently asked questions
What’s the difference between AI literature review tools and AI systematic review tools?
General AI literature review tools are built for open-ended discovery, citation mapping, and synthesis across a body of work. AI systematic review tools are built around the specific, documented PRISMA workflow – deduplicated search import, dual independent screening with conflict resolution, structured extraction forms, and risk-of-bias appraisal – that a formal systematic or scoping review has to report.
Is Rayyan or Covidence better for a systematic review?
Rayyan is free (with paid tiers for larger teams) and strongest at the screening stage specifically. Covidence covers the full review workflow – screening, extraction, and risk-of-bias – in one connected tool and is the platform Cochrane recommends for new Cochrane reviews, but it’s a paid, per-review product. Teams doing a Cochrane-style review with institutional access often use Covidence end-to-end; teams that just need collaborative screening, or don’t have a Covidence license, often use Rayyan for that stage alone.
Do AI screening tools replace dual independent screening?
No. PRISMA and Cochrane methodology expect two reviewers to independently screen records and resolve disagreements, and tools like Rayyan and Covidence are built to support that process – blinding reviewers from each other’s decisions, then surfacing conflicts – rather than to replace it with a single automated pass.
Is ASReview free to use?
Yes. ASReview is open-source software from Utrecht University, free to download and run, which distinguishes it from Covidence and DistillerSR’s commercial licensing models.
Do these tools work for scoping reviews as well as systematic reviews?
Most of them do – Rayyan, Covidence, and ASReview are all used for scoping reviews, which follow a similar screening-and-extraction structure under the PRISMA-ScR extension. See literature review vs. systematic review vs. scoping review for how the review types and their reporting requirements differ.
