Use Case · Preprint Fact-Check

Fact-check a biomedical preprint's claims, citations, and effect sizes against the literature

PubMed-grounded biomedical AI that verifies an uploaded preprint, manuscript, or proposal against the published record — which claims are supported, which are overstated, and what's missing. Built for peer reviewers, editors, PIs, and authors pressure-testing a draft before submission.

Fact-check your preprint

Upload a preprint, manuscript, or proposal PDF and BioSkepsis verifies its core claims against the published literature — each one tagged validated, partially supported, or not reported, with PMID-grounded evidence.

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Built for peer reviewers, editors, PIs, and authors

Peer reviewers & editors: triage a submission's claims, citations, and methods fast, with PMID-grounded evidence behind every verdict.
PIs & authors: pressure-test your own draft before submission — catch overstated effect sizes and weak novelty claims before a reviewer does.
Research-integrity & due-diligence teams: spot selective citation, inflated statistics, and unsupported attributions.

The 9-step workflow — upload to verified verdict

Upload the document, then move from a broad fact-check to targeted probes: re-verify the core claim, check the numbers, hunt for omitted contradicting evidence, classify the mechanism, audit for selective citation and method reliability, and close by checking sample sizes, attributions, missing references, and the novelty claim.

Upload the document and request a fact-check of its core mechanistic claims

Attach a PDF (preprint, manuscript, or proposal) and ask BioSkepsis to verify its central claims against the published literature, providing relevant citations for each validated or challenged assertion.

Example query "Fact check this preprint article and give me all relevant citations"

Review the initial fact-check synthesis

BioSkepsis returns a structured verification organised by the document's main mechanistic claims. Each claim is tagged as validated, partially supported, or not reported (NR) with PMID-grounded evidence. Check the unverified citations panel — this is where the system catches mismatches between what the document asserts and what the cited papers actually show.

Request a deeper verification of the core claim

Ask BioSkepsis to re-verify the central mechanistic chain with more granularity. This second pass often surfaces additional supporting or contradicting evidence that the first pass did not fully elaborate.

Example query "Verify the core claim"

Verify cited effect sizes and quantitative claims

Ask BioSkepsis to check the specific numbers, statistics, and quantitative results the document cites. This catches inflated effect sizes, misquoted p-values, or figures that do not match the source papers.

Example query "Check cited effect sizes"

Identify contradicting or qualifying evidence the authors did not cite

Ask what published studies contradict or qualify the document's main conclusions that were not included in its reference list. This exposes selective citation.

Example query "What published studies contradict or qualify the main conclusions of this preprint that we did not cite?"

Assess whether the proposed mechanism is established, contested, or speculative

Ask BioSkepsis to evaluate each link in the proposed mechanistic chain and classify it as well-established, contested, or speculative based on the broader literature.

Example query "The preprint proposes a specific biological mechanism. What does the broader literature show about this mechanism — is it well-established, contested, or speculative?"

Check for selective citation bias

Ask whether the document's references are representative of the field or whether the authors have selectively cited papers that support their argument while ignoring those that do not.

Example query "Are the references in this preprint representative of the field, or has the author selectively cited papers that support their argument while ignoring those that don't?"

Evaluate the reliability of the experimental methods

Ask what the literature says about the reliability, limitations, and known pitfalls of the specific experimental methods or analytical approaches used in the document.

Example query "The preprint uses a specific experimental method or analytical approach. What does the literature say about the reliability, limitations, and known pitfalls of this method?"

Verify sample sizes, gene/protein attributions, missing citations, and novelty claims

Close the fact-check by probing four remaining dimensions: whether the sample sizes and power claims are adequate, whether the document's attribution of function to specific genes or proteins is confirmed by the literature, what the authors should have cited but did not, and whether the novelty claim holds or has been undermined by prior work.

Example query — sample size "Verify sample size and power claims"

Example query — attribution "The preprint attributes a specific function or disease association to a gene, protein, or variant. Does the published literature confirm this attribution, or is the evidence weaker than presented?"

Example query — missing citations "Identify what the authors should have cited but didn't"

Example query — novelty "The preprint claims this finding is novel or first-of-its-kind. Has anything similar been published before that would undermine that claim?"

What you walk away with

Claim-by-claim verdict

Each main claim tagged validated, partially supported, or not reported — every verdict PMID-grounded.

Effect-size & stats check

Inflated effect sizes, misquoted p-values, mismatched figures, and weak sample-size or power claims caught.

Selective-citation audit

Contradicting or qualifying evidence the authors omitted, plus what they should have cited but did not.

Methods & novelty assessment

Reliability and pitfalls of the techniques used, and whether the "first-of-its-kind" claim actually holds.

Run the fact-check on your own document

Upload a preprint, manuscript, or proposal and get a claim-by-claim verification against the literature — validated, partially supported, or not reported, with verified citations.

Upload & fact-check free →

Honest limits — what AI will not do when fact-checking

BioSkepsis verifies against what is published and indexed. It cannot check raw data, rerun analyses, or detect fabrication that the literature does not contradict.
A "not reported" (NR) tag means the corpus does not confirm a claim — not that it is false. Absence of supporting evidence is not proof of error.
It assists peer review; it does not replace it. The reviewer's judgement and the journal's process remain the decision-makers.
Verify each flagged citation against the source before acting on it in a review, rebuttal, or revision.

Frequently asked questions

What document types can I upload to fact-check?

Any biomedical preprint, manuscript, or research proposal as a PDF. BioSkepsis verifies the document's claims against its 40M+ paper biomedical corpus (PubMed, bioRxiv, medRxiv) and returns PMID-grounded evidence for each validated or challenged assertion.

What do the validated, partially supported, and not reported (NR) tags mean?

Each claim is tagged by how well the published literature supports it: validated (well-supported by cited evidence), partially supported (some support, with caveats or weaker evidence than presented), or not reported / NR (the corpus does not confirm it). NR means the system could not find supporting evidence — not that the claim is necessarily false.

Can it catch selective citation and missing references?

Yes. Steps 5 and 7 ask which published studies contradict or qualify the document's conclusions that were not cited, and whether the reference list is representative of the field or skewed toward supporting papers. Step 9 also asks what the authors should have cited but did not — surfacing selective-citation bias.

Does it check effect sizes and sample sizes?

Yes. Step 4 verifies cited effect sizes and quantitative claims against the source papers — catching inflated effect sizes, misquoted p-values, and figures that do not match. Step 9 separately probes whether sample sizes and statistical-power claims are adequate.

Is this for authors or for reviewers?

Both. Authors use it to pressure-test their own draft before submission — checking that claims, effect sizes, and novelty hold up. Peer reviewers and editors use it to triage a submission's claims, citations, and methods quickly. It assists the review; it does not replace the reviewer's judgement or the journal's process.

Fact-check your next manuscript

PubMed-grounded biomedical AI. Verified citations. Claims, effect sizes, methods, and novelty checked against the literature. Free tier — no credit card.

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