Finding papers with Elicit:useful tables, human screening decisions
You type in a research question, and out comes a long list of related papers with their key points arranged in a table — the first time you use a research-support AI like Elicit, you might think, "I could finish the groundwork for a literature review in one night." And for groundwork, it really is powerful. Whether you can use that table directly in your research, though, is another matter. The table is a list of papers you might read — not a record of papers you have read. With that distinction in mind, here's when and how to use Elicit.
Published: 2026-07-22
What Elicit does
Elicit is a tool that takes a research question, finds relevant papers, and organizes each paper's key points — participants, methods, results, and so on — into a table (details of the features change over time, so I'll focus on the concept).
Because you search with a question rather than a keyword combination, its greatest strength is that you can reach candidate literature even before you've crafted good search terms.
What it's good for: seeding a literature list
Here are the situations where it fits well.
The early stage of a literature review, when you want to surface relevant papers quickly; catching papers your search terms missed; and confirming that similar research exists before you commit to a study plan. Think of it as a reconnaissance unit that runs alongside traditional database searches — not a replacement for them.
Don't take the table's numbers and summaries on faith
Convenient as Elicit's tables are, they contain information extracted from papers automatically by AI — which means extraction errors, misreadings, and missing context are always possible.
Never copy numbers or conclusions from the table straight into a review or a presentation. The table helps you decide which papers to read; it is not an authoritative record of what those papers say.
Check for drift against your PICO/PCC
Even papers that look relevant in the table can turn out, on closer inspection, to be misaligned with your research question.
Do the participants (P) match in age and severity? Are the interventions and concepts (I/C) defined the same way? Is the outcome (O) actually the measure you care about? When you scan a row of the table, take one extra beat to compare it against your own PICO (or PCC) — that single habit will spare you hours of reading papers you never needed to read.
For papers that interest you, go to the original PDF
Once you've narrowed the candidates, the next step is to get the original PDF and actually read it.
Upload it to Paperfy and each paper gets its AI summary, figures, tables, and original text on one page. Test the first impression you formed from Elicit's table against the original text, and where they diverge, edit the summary by hand and keep it as your own notes. That's the moment "information from a table" becomes "verified understanding."
In systematic reviews, humans stay in control
If you're bringing Elicit into a full systematic review, the bar rises further.
Search reproducibility, recorded inclusion/exclusion decisions and reasons, data extraction, risk-of-bias assessment — these are processes owned and reviewed by people and teams who check the original text, not tool outputs. Describe Elicit honestly as an aid to candidate gathering, and keep the decisions human until the very end (for where the boundaries of AI use in reviews lie, see our [article on AI in systematic reviews](https://paperfy.app/en/articles/systematic-review-ai-tool)).
In short: Elicit for reconnaissance, the original text for confirmation
Elicit's value is the speed with which it gets you to reading candidates. Everything after that — confirming, judging, recording — is the work of the original text and human judgment.
Respect that division of labor, and Elicit becomes a dependable partner that dramatically cuts the time you spend hunting for literature.
Elicit usage checklist
- I treat the table as a list of reading candidates, not as confirmed information.
- I didn't transcribe numbers from the table directly into a review or presentation.
- I compared each candidate against my own PICO/PCC for drift.
- I read the candidate papers in the original PDF and kept notes.
Verify the papers you found in the table — then keep them.
Candidates you surface with Elicit become a package of original text, summary, and figures and tables in Paperfy. Turn the table's information into verified understanding.
Literature review
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