Systematic Review Automation: Elicit vs ASReview LAB

Systematic-review automation now comes in two very different flavours: Elicit’s Systematic Review workflow, a closed-source “AI research assistant” that chains generative models around a fixed review pipeline, and ASReview LAB, an open-source framework that uses active-learning algorithms to prioritise records interactively. Both tools accelerate the classic search → screen → extract → synthesise loop, but they diverge in how much you can customise the AI, how transparent the ranking logic is, and where the time-savings occur. The comparison below drills into each step of the workflow, highlights published performance data, and lays out when you might pick one over the other.

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1 | Core Workflow at a Glance

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2 | Search & Import Stage

Elicit

• Enter a PICO-style question; the tool retrieves and ranks literature from its 125 million-paper index, then augments with any PDFs you drag-and-drop. (support.elicit.com, The Elicit Blog)

ASReview

• You bring the corpus (RIS, EndNote XML, etc.). The strength is neutrality: it lets teams merge PubMed, Embase, Scopus and grey-literature dumps without vendor lock-in. (asreview.readthedocs.io, ASReview)

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3 | Title & Abstract Screening

• Elicit pre-runs a transformer classifier and shows a pilot set so you can correct edge cases before committing to a larger batch. (The Elicit Blog)
• ASReview starts with a handful of user-labelled records; after each click, an active-learner retrains and resurfaces the next most-informative paper, rapidly homing in on inclusions. Published validation shows workers found 95 % of relevant studies after screening just 8–33 % of titles (83 % mean labour saved). (Nature)

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4 | Full-Text Extraction

• Elicit auto-populates up to 20 customisable columns (sample size, effect size, design, etc.) and hyperlinks every cell to the quote in the PDF, giving an instant audit trail. (elicit.com)
• ASReview focuses on screening; extraction is out of scope but can be added via its Python API or community plug-ins—ideal if you prefer full control or niche data fields. (asreview.readthedocs.io)

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5 | Algorithms & Transparency

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6 | Performance & Accuracy Evidence

• Elicit has not yet published peer-reviewed benchmarks, but internal docs promise background processing of >1 000 PDFs and pilot testing to mitigate false negatives. (support.elicit.com)
• ASReview is validated in multiple peer-reviewed studies: Nature Machine Intelligence article (2021) reports 67-92 % workload reduction at 95 % recall; education-psychology simulation confirms similar gains. (Nature, SpringerLink)
• Independent ergonomics studies show semi-automated tools like ASReview can “halve the screening workload while achieving high recall levels of 95 % and above”. (PsychArchives)

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7 | Collaboration, Reproducibility & Compliance

• Elicit stores projects in the cloud; share links let co-authors view decisions but cannot fork the underlying model.
• ASReview supports multiple screeners, crowdscreening, and Git-versioned project files, aligning better with open-science and PRISMA-2020 reporting. (ASReview)

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8 | Strengths & Limitations

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9 | Choosing Between Them

• Pick Elicit if you need a turn-key rapid review with built-in extraction tables and are comfortable with a commercial SaaS.
• Choose ASReview when you want full transparency, custom ML models, or to embed the screening engine inside an academic workflow, and you’re prepared to handle search/export outside the tool.

Both can dramatically shorten the road to a trustworthy systematic review, but they trade off between managed convenience (Elicit) and open, reproducible control (ASReview).