Elicit AI Can Now Do Your Entire Systematic Review (In Minutes)

Elicit AI’s paid “systematic review workflow” can screen hundreds of papers and extract structured data in minutes—turning what normally takes hours or days of manual reading into a guided, step-by-step process with adjustable inclusion thresholds. In a live walkthrough using a mindfulness-and-anxiety research question, the tool pulled in 499 candidate papers, evaluated them against inclusion/exclusion criteria, and then extracted data from the subset that met the user’s relevance bar.

The workflow starts with a research question and then asks for a speed/comprehensiveness setting (the walkthrough used a “fast” option). Elicit generates a research report by first finding papers, then screening them, and finally extracting data. A key feature is the structured “systematic review” pipeline: it offers suggested screening steps, lets users choose whether to use AI-generated suggestions or skip directly to screening, and supports importing papers already found elsewhere (including uploading up to 500 PDFs). The interface also exposes screening dimensions—such as age range, data separation, intervention duration/type, and related study characteristics—so inclusion decisions aren’t a black box.

During screening, Elicit evaluates each paper and assigns an inclusion score. The walkthrough showed the system processing all 499 papers autonomously, with progress updates and counts of papers likely to be included versus excluded. Once screening completes, the user can tune the score threshold using a slider to control how strict the review is. In the example, a high score (near 4.9 out of 5) signaled very close alignment with the research question, while lower scores corresponded to weaker matches. Adjusting the threshold changed the number of included studies dramatically—down to a single paper at very high thresholds, or up to 107 papers at a lower cutoff.

After inclusion is set, Elicit performs data extraction across the included studies. It can use suggested extraction columns (including study design and randomization details) or add custom fields. The walkthrough emphasized that extraction is where most of the time goes, because it pulls detailed information per paper—participant demographics, intervention details, and outcome/effect-size elements—then compiles everything into a report.

The results appear as both an on-screen report and downloadable PDF. The report includes typical systematic-review components: an abstract, methods and flow-style accounting of how many papers were screened, included, and ignored, plus tables of outcomes and secondary outcomes. The walkthrough also noted that the full report generation can take roughly 10–20 minutes depending on reference counts.

Beyond systematic reviews, Elicit retains its broader research workflow: semantic paper search, research-question-driven summaries, filtering, and iterative steps like creating tables, summarizing concepts across papers, and “chat with papers” using full text (not just abstracts). The overall pitch is practical: a guided pipeline that forces the same decisions researchers make—while automating the bulk of screening and extraction.