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Verify an AI-generated review in two separate passes: first confirm that every cited paper exists and its bibliographic details match; then test each factual statement against the paper’s methods, results, tables, figures, and limitations. A real citation does not make the attached summary accurate.
The workflow below turns a fluent review into an auditable set of claims. It shows what to check, how to record the result, when to seek other studies, and how to handle disclosure and confidentiality.
What you are actually verifying
An AI-generated review can fail in several independent ways. The reference may be fabricated, or it may identify a real paper but attach the wrong title, authors, year, journal, or DOI. Even a perfectly matched citation may not support the sentence next to it. The model may have confused correlation with causation, generalized beyond the study population, omitted a null result, or left out a limitation that changes the interpretation.
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- Does the source exist? Can you locate the publication in a publisher record, library catalogue, DOI record, or scholarly database?
- Does the metadata match? Do the authors, title, venue, year, volume, issue, pages, and DOI identify the same work?
- Does the cited passage support the claim? Is the wording supported directly, only with important qualifications, contradicted, or not addressed?
- Is the study represented faithfully? Are its question, design, sample, measures, outcomes, analysis, uncertainty, and limitations described without distortion?
- Does the wider evidence agree? For an important or disputed conclusion, do other studies corroborate or challenge the interpretation?
Oxford Bodleian Libraries puts the basic responsibility plainly: “You should not include anything in your references or bibliography that you have not actually consulted.” Consulting a paper means checking the original publication, not merely accepting an AI-generated bibliography or an abstract paraphrase.
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A seven-step verification workflow
1. Build a source list before judging the prose
Copy every in-text citation and bibliography entry into a working sheet. Give each reference an ID (for example, R1, R2, and R3), and note every sentence or paragraph that uses it. This prevents a single citation at the end of a long paragraph from being treated as support for every proposition in that paragraph.
Record the citation exactly as generated, including spelling, punctuation, URL, DOI, and access date. Preserve the original AI output separately so that later edits can be traced.
2. Confirm that each paper exists
Search the full title and author combination in this order:
- the journal or conference publisher’s record;
- a DOI registry record or the DOI resolver;
- your library catalogue or a recognised scholarly database; and
- an institutional repository or preprint server when the work is openly archived.
Do not accept a plausible-looking title as proof. Generated references can be entirely fabricated, can combine details from two papers, or can point to a real author who never wrote the cited work. If no authoritative record appears, mark the reference unverified and remove any claim that depends on it until you locate the underlying source.
3. Match all available bibliographic details
Compare the AI entry with the authoritative record, field by field:
| Field | What to compare | What a mismatch can indicate |
|---|---|---|
| Authors | Names, order, initials, and group authorship | A different paper, a merged author list, or a misspelled identity |
| Title | Exact wording, subtitle, and version | A fabricated title or confusion between an article, protocol, editorial, and correction |
| Venue | Journal, conference, book, repository, or report | The citation has been attached to the wrong publication type |
| Publication date | Online-first date versus issue date | Anachronistic claims or a later correction being overlooked |
| Volume, issue, and pages | Values shown in the publisher or library record | A blend of metadata from multiple records |
| DOI or persistent identifier | Identifier resolves to the same title and authors | A dead, mistyped, or unrelated identifier |
When a paper has a correction, retraction, expression of concern, protocol, or preprint and published version, record which version the review actually used. A correct title with the wrong version can change the reported sample, results, or conclusions.
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4. Open and read the source paper
Read the abstract to orient yourself, then open the full text. For a low-stakes background statement, the abstract may identify the relevant result; for any consequential claim, inspect the methods, results, tables, figures, supplementary material, and limitations. Abstracts often compress eligibility criteria, exclusions, statistical uncertainty, and null findings that are decisive for interpretation.
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5. Split the review into atomic claims
Rewrite each sentence as one or more propositions that can be checked independently. Consider this sentence:
“The intervention reduced anxiety in adolescents, proving it works for schools and producing a large, lasting effect.”
It contains at least four claims:
- the study tested an intervention;
- anxiety was measured in adolescents;
- the observed result was a reduction; and
- the evidence establishes effectiveness in schools and a large, lasting effect.
Those propositions may require different passages, and the final two may not follow from the design at all. For each atomic claim, record the cited source and the exact section, table, figure, page, or result that bears on it. Classify the outcome as supported, partly supported, contradicted, or unresolved.
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6. Compare scope, strength, and uncertainty
Read the paper as a study, not as a collection of quotable conclusions. Check the following elements against the AI wording:
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- Question and setting: What population, place, period, or material did the authors study?
- Design: Was it randomised, observational, qualitative, laboratory-based, computational, a systematic review, or another design?
- Sample or data: How many participants, records, experiments, or documents were analysed, and who or what was excluded?
- Measures: Which exposure, intervention, outcome, instrument, proxy, or definition was used?
- Analysis: Which model or comparison produced the result, and were adjustments, subgroup analyses, or sensitivity checks used?
- Results: What estimate, direction, effect size, interval, probability value, or qualitative pattern was actually reported?
- Uncertainty: How precise is the estimate, and what assumptions or missing data affect it?
- Limitations: What weaknesses do the authors identify, and which ones did the AI leave out?
Use the authors’ distinction between observation and interpretation. An association in an observational study does not by itself establish that one variable caused another. A result in one age group, country, species, dataset, or controlled setting does not automatically apply to a wider population. A statistically detectable difference may be too small to matter in practice, while a wide interval may leave the direction or size uncertain.
7. Seek corroboration and keep an audit trail
A paper can be summarized accurately and still be only one part of the evidence. For high-stakes, novel, or contested statements, search for independent studies that support, qualify, or challenge the finding. Also ask what relevant sources the AI did not suggest. Oxford’s guidance specifically recommends checking corroboration rather than treating an AI-selected bibliography as complete.
For each checked claim, preserve:
- the original wording and citation;
- the source location you consulted;
- your classification (supported, partly supported, contradicted, or unresolved);
- the reason for the classification;
- the corrected wording, if needed; and
- the reviewer, date, and version of the document.
Keep the audit sheet with the manuscript or project record. It lets another editor reproduce your decision and prevents a later revision from silently reintroducing a rejected claim.
Use a claim-check matrix
A matrix makes omissions visible and separates metadata checks from substantive checks. One row should represent one atomic claim, not an entire paragraph.
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|---|---|---|---|---|---|---|
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| C2 | Next atomic proposition | R2 or “no valid source” | Precise location or “not found” | Relevant design and sample details | Relevant caveat or uncertainty | Action, owner, and date |
Do not use “citation present” as the decision. A citation can be real while the claim is unsupported. Conversely, a claim may be reasonable but still require a different source if the cited paper did not study that question.
How to detect common AI misrepresentations
Fabricated or blended references
Warning signs include a DOI that resolves to another article, a journal volume that did not exist in the stated year, an unusually generic title, or author combinations that appear nowhere in the publisher record. A reference that combines a real title with the wrong authors or pages is not “close enough”; replace it with the verified record or delete it.
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Misquotation and context loss
Search the paper for the quoted words and read the surrounding paragraph. AI systems can turn a limitation, hypothesis, or future-work statement into a finding. They can also remove qualifiers such as “in this sample,” “may,” “was associated with,” or “under the tested conditions.” Preserve qualifiers that change the claim’s meaning.
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Causal overreach
Look for verbs such as caused, led to, prevents, or proves when the design only reports an association or prediction. Replace them with the paper’s actual language, such as was associated with, was consistent with, or predicted, unless the design and analysis justify a causal interpretation.
Population and setting inflation
Check whether the review changes adults to children, a clinical sample to the general public, one country to “globally,” or a laboratory material to a real-world product. The same outcome measure can have different meanings across populations and settings.
Numerical and statistical errors
Recalculate or transcribe numbers directly from the results table. Distinguish an absolute change from a percentage change, a relative risk from an odds ratio, and a confidence interval from a standard deviation. Check whether the AI has reversed the comparison group, dropped a minus sign, rounded away a wide interval, or presented a secondary analysis as the primary result.
Omitted null results and limitations
Read the full results section and the authors’ limitations, not only the abstract’s positive sentence. A review that reports a statistically significant subgroup while omitting a null primary outcome gives a misleading impression even if every individual number is copied correctly.
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Version, correction, and retraction confusion
Verify whether the cited work is a preprint, accepted manuscript, final version, correction, or retraction notice. If a correction changes a number or conclusion, cite and describe the corrected record. Do not present a retracted finding as current evidence without clearly explaining its status.
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When automated checking helps—and when it does not
Software can accelerate mechanical tasks such as extracting citations, resolving identifiers, locating repeated phrases, and highlighting claims that lack a nearby reference. It cannot substitute for a human reading the source and deciding whether a method, limitation, or qualifier has been represented faithfully.
The OECD’s 2023 discussion of scientific-claim verification describes two useful benchmark resources: SciFact contains 1,409 claims checked against more than 5,000 scientific-paper abstracts, and COVID-Fact contains 4,086 claims with associated evidence documents. These are dataset sizes, not accuracy rates and not evidence that an individual AI review is reliable. The OECD also describes continuing difficulty applying automated verification to scientific language and warns that people may over-trust incorrect model predictions.
Use automated output as a queue for human inspection. Require a reviewer to open the paper, confirm the passage, inspect the design and limitations, and sign off on the final wording. Never label a claim “verified” merely because a checker returned a high score or found a lexical match.
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Before uploading a manuscript, peer-review report, unpublished dataset, patient information, or proprietary document to an AI service, check the service terms and your institution’s rules. ICMJE guidance highlights confidentiality risks when submitted manuscripts are entered into systems without assured protections. CDC guidance advises against putting sensitive, protected, or non-public data into public tools.
Disclosure requirements vary by publisher, institution, funder, discipline, and jurisdiction. Check the current policy that governs your work. APA’s guidance recommends retaining prompts and, where appropriate, outputs. CDC recommends documenting the tool, its purpose, the content affected, and the human oversight applied. The European Commission’s 2025 living guidelines likewise place responsibility on researchers to use generative AI responsibly and transparently.
Do not assume that disclosure cures an inaccurate citation. Transparency records how a tool was used; the author or editor remains responsible for the accuracy of the resulting text.
A practical correction protocol
- Freeze the source version. Save the AI output, the manuscript version, and the records you consulted.
- Mark the problem locally. Highlight the exact phrase rather than deleting an entire paragraph before its dependencies are understood.
- Classify the error. Use a specific label such as fabricated source, metadata mismatch, unsupported inference, wrong number, omitted limitation, or unresolved access.
- Rewrite to the evidence. Retain the study’s population, design, result, and qualifiers; remove claims the paper does not establish.
- Recheck dependent sentences. A changed effect direction or study population may require edits elsewhere in the review.
- Seek a second reviewer for consequential claims. Independent checking is especially valuable when the conclusion affects health, safety, policy, funding, or legal decisions.
- Record the final decision. Add the corrected wording, evidence location, reviewer, and date to the claim matrix.
Worked example of the reasoning process
Imagine a review says: “Paper R1 proves that a training program improves employee productivity by 20% across industries.” A disciplined check would not begin by polishing the sentence.
- Confirm that R1 exists and that its authors, title, venue, year, and identifier match.
- Locate the productivity outcome and determine whether “20%” is an absolute difference, a relative estimate, or a model coefficient.
- Read the methods to identify the industries, sample size, comparison group, follow-up period, and whether assignment was randomised.
- Check whether the paper measured productivity directly or used a proxy.
- Read the limitations and results for missing data, attrition, subgroup restrictions, and uncertainty around the estimate.
- Replace “proves” and “across industries” unless the design and data justify those statements.
- Search for other studies before presenting the result as a general conclusion.
The final wording might need to describe an estimate in the studied sample and period, with its interval and limitations, rather than a universal effect. The example is a method illustration; it is not a claim about a particular training program.
Final sign-off checklist
- Every reference resolves to a real, consulted source.
- Authors, title, venue, date, and identifier match the version used.
- Every compound sentence has been split into atomic claims.
- Each claim has a source passage, table, figure, or explicit unresolved status.
- Population, setting, design, sample, measures, outcomes, and analysis are represented accurately.
- Numbers, comparison groups, units, intervals, and statistical terms have been checked.
- Observations are not presented as causal or general conclusions without support.
- Material limitations, null findings, and uncertainty remain in the review.
- Important or contested claims have corroborating and challenging evidence.
- Confidentiality, disclosure, and record-retention rules for the project have been followed.
- A human reviewer has approved the corrected text and retained the audit trail.
ICMJE summarizes the governing principle: “Because AI can generate authoritative-sounding output that can be incorrect, incomplete, or biased, humans are ultimately responsible for reviewing and ensuring the accuracy of any content generated with assistance of AI.” CDC similarly cautions that “citation inaccuracies can be considered research misconduct.” Treat those statements as operational requirements: verify the source, verify the claim, and document the human judgment that connects the two.
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