Elicit AI Review

What Is Elicit AI?

Elicit AI is an AI research assistant built for scientific literature. Type a research question and it searches over 138 million academic papers and clinical trials, screens the most relevant results, and produces a structured report with every claim cited at sentence level.

It runs on a 'factored cognition' approach: search, screening, extraction, and reporting work as separate, validated steps rather than one opaque model. That makes outputs traceable and auditable — essential for high-stakes academic and industry research.

Since 2018 the platform has been used by over 2 million researchers in academia and industry. It now also offers an API and MCP server so its pipeline can power agents and custom research workflows.

The core promise is simple: turn days of manual literature work into minutes of assisted review, without sacrificing the citations reviewers need to verify every claim.

The free tier is enough to test the workflow on a small review, and the monthly credit pool refreshes automatically. Most researchers start there before deciding whether the paid tiers are worth it for their workload.

Elicit AI Literature Review Tool

The literature review workflow automates the slow parts of reviewing research. You start with a question, and the platform searches, screens, and organizes relevant papers into a working list you can iterate on.

Screening is assisted too: mark papers as included or excluded, filter by study type, year, and quality signals, and export the whole review. The systematic review mode aligns with PRISMA 2020 and keeps every stage reproducible.

For teams, reviews can be shared and handed between members, with roles that keep screening consistent. Version history preserves what changed at each stage, which reviewers appreciate during audits.

• Search across 138M+ papers and clinical trials
• Assisted abstract and full-text screening
• Systematic review workflow with PRISMA 2020 support
• Exportable, auditable review stages

Elicit AI Extract Data from PDF

Extraction is where the tool shines. Give it a batch of papers and it reads each PDF and pulls structured fields — population, intervention, sample size, outcomes, and effect estimates — into a table you can filter and sort.

Because extraction is a separate validated step, the output stays consistent across hundreds of papers. You can review, correct, and re-run extractions, which keeps errors visible instead of hiding them in a black box.

The extraction tables export to CSV and reference managers, so findings can move straight into analysis software. Accuracy improves when you provide clear instructions for each column, and corrections teach the schema over time.

• Pull structured data from uploaded PDFs
• Population, sample size, outcomes & effects
• Consistent extraction across hundreds of papers
• Review and correct any extracted value

Elicit AI Synthesize Papers

Synthesis takes the extracted data one step further: the tool writes an AI summary that compares findings across the included papers, highlights where studies agree and diverge, and supports every statement with citations.

The result reads like a research summary, not a chatbot ramble — organized by theme or question, with a limitations section you can expand and edit. It's a strong starting draft for a methods section or review manuscript.

Summaries default to plain language so stakeholders outside the lab can follow the evidence. That broadens who can act on the research, from clinicians to policy teams, without losing the citation trail.

• Cited synthesis across your included papers
• Theme-based summaries with diverging findings
• Editable limitations section
• Draft-ready for manuscripts

Elicit AI Custom Columns

Custom columns let you define the exact data you want extracted. Instead of default fields, create columns like 'Blinding', 'Primary outcome', or 'Funding source' and the tool extracts them for every paper in your review.

Columns persist across sessions, so a team can standardize an extraction schema once and reuse it on every new batch of papers. That turns scattered reading notes into a consistent, queryable dataset.

Because schemas live with your workspace, onboarding new collaborators is fast — they inherit the exact fields your team agreed on. It also reduces the chance of inconsistent data when different people review different papers.

• Define your own extraction schema
• Reusable columns across reviews and teams
• Filter and export column data
• Consistent dataset from mixed sources

Elicit AI Pricing — Free Credits & Subscription

The platform runs on a credit model: a monthly pool powers searches, summaries, and extractions. The free tier gives a starter pool every month, enough to evaluate the workflow on a small review.

Plus from around $12/month raises the pool and unlocks full extraction and longer reports. Pro and Team plans add the API, systematic review workflows, and admin controls, with pricing quoted per team. Heavy review periods may need a higher plan.

Credits renew monthly on every plan, and unused Pro credits can roll over on annual billing. For teams, per-seat plans pool usage centrally, which is convenient when several researchers share a literature review project.

Elicit vs Consensus AI

Consensus AI answers scientific questions fast with an agreement Meter and cited summaries, ideal for a quick evidence read. It is optimized for breadth — checking what the science says in seconds.

This platform goes deeper into the review workflow: structured data extraction, custom columns, and systematic screening. If you need a defensible extraction table across hundreds of papers, the platform is the stronger option; for a fast clinical answer, Consensus wins.

Both tools give you a generous free tier to compare on your own questions. The deciding factor is usually the workflow: quick answers versus a structured extraction pipeline you can defend.

Elicit vs Scite.ai

Scite.ai is best known for its Smart Citations and citation statements — it shows how papers cite each other and whether citations are supporting, mentioning, or contrasting. That makes it a strong tool for citation analysis and reference checks.

This platform is built around extraction and synthesis rather than citation graphs. If your work is about how literature interacts, Scite adds real value; if you need structured data from papers, the extraction-first approach here is more direct. Many reviewers end up using both.

For a hands-on comparison, run the same set of papers through both platforms and inspect the outputs. Citation statements are Scite's edge, while reproducible extraction tables are the strength here.

Elicit vs Perplexity

Perplexity is a general AI search engine for fast, cited answers from the live web. It covers news, code, and everyday knowledge with a generous free tier.

The platform is a specialist for academic evidence: it searches a curated scholarly corpus, screens papers systematically, and extracts structured data for reviews. For a quick answer, Perplexity is faster; for a defensible literature review, the research-first approach here is in a league of its own.

The free tiers of both make the test easy: search the same research question and compare how each handles citations and structured output. Most heavy researchers keep both, using Perplexity for breadth and this platform for depth.

Pros and Cons

Here's a quick summary of where the platform shines and where it falls short.

Weight the trade-offs by how much structured extraction you need — heavy review work justifies a paid plan, while light users can stay on the free tier.

Before upgrading, run a real question through the free tier and check whether the extraction quality and credit ceiling match your volume. That keeps the decision based on your actual workflow rather than a feature list.

Best For

Recommended use cases and scenarios where Elicit shines.

Frequently Asked Questions

Common questions about Elicit, answered.