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Do not trust an AI-generated scientific claim just because it sounds precise or includes a citation. Check the claim against the source it cites, assess that source’s quality and context, look for later and independent evidence, and state uncertainty honestly. For consequential or technically difficult claims, ask a relevant expert to review your conclusion.
Start by breaking the statement into checkable claims
A sentence may combine a result, an explanation and a broad conclusion. Separate those parts so each can be verified independently. Distinguish a factual statement from an interpretation, prediction or opinion: they require different kinds of evidence. The OECD’s chapter on scientific claim verification describes claims as verifiable statements, while cautioning that verification is not always a simple true-or-false exercise (OECD, 2023).
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For example, “A new treatment reduces symptoms and is safe for everyone” contains at least two claims: that it reduces symptoms and that it is safe for everyone. The first needs evidence about the measured outcome; the second requires evidence broad enough to support a claim about safety across different people. Do not let evidence for one part stand in for the other.
Verify the citation before relying on it
Open every cited link. Confirm that the document exists and that its title, authors or issuing organization, publication venue and date match what the AI says. A convincing-looking reference is not proof: generative AI can invent citations, and real references can be attached to claims they do not support. Guidance from the CDC and NIH highlights citation accuracy and the possibility of fabricated references (CDC, May 28, 2026; NIH and HHS Office of Research Integrity, May 14, 2026).
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- If a link is broken, search the exact title in the publisher’s or issuing body’s own site or catalogue.
- Check that the cited item is the document described, not a different paper with a similar title or an unrelated source.
- Note whether it is a peer-reviewed paper, preprint, review, official dataset or another kind of material. The distinction affects what conclusions it can support.
Read the source in context
Do not stop at the title or abstract. Locate the relevant passage and, where needed, check the methods, results and limitations. Ask whether the source matches the AI’s claim on the details that matter:
- Population: Were the people, animals, cells or other subjects studied the same as those covered by the claim?
- Intervention or exposure: Was the treatment, condition or exposure the same?
- Outcome: Did the source measure the outcome being asserted, or a related but different one?
- Time and setting: Does the study period or setting fit the claim?
- Certainty: Does the source support a firm conclusion, or only a limited association or preliminary result?
A paper about a related topic is not automatically evidence for the exact statement. If the claim generalizes beyond the study’s participants, methods or measured outcomes, make that gap explicit rather than repeating the broader wording.
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Assess the evidence, not just the publication label
Publication status is useful context, not a shortcut to a verdict. Consider how the evidence was produced and checked. USGS describes its science practices as including data-quality assurance and validation, methodological transparency and rigorous peer review (USGS Office of Science Quality and Integrity).
- Look for clear methods and enough information to understand how the data were collected and analyzed.
- Check whether the measurements and data were validated and whether the conclusions follow from the reported results.
- Read limitations, including small or unrepresentative samples, possible bias, uncertainty and conflicts that may affect interpretation.
- Distinguish an individual study from a systematic review, official dataset or other synthesis; each has a different scope and evidentiary role.
A peer-reviewed study can still have limitations, and a preprint may contain useful but not yet peer-reviewed findings. Describe the evidence in those terms instead of treating any one label as a guarantee of reliability.
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Check for newer findings and evidence on both sides
Scientific conclusions can change as researchers validate, replicate or challenge findings. Search for later studies, official updates and independent sources, especially when the claim concerns a fast-moving field. Check whether the AI has relied on an older result that subsequent work has qualified.
Look for evidence that supports the claim as well as evidence that challenges it. The OECD notes that scientific evidence can be contradictory and that individual results may offer only limited evidence toward a claim. A single study rarely settles a complex question by itself. Record meaningful disagreements and limits; do not cherry-pick the one source that matches the AI’s answer.
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Reach a conclusion that matches the evidence
There is no universal score or checklist threshold that can turn every scientific claim into a reliable yes-or-no answer. The right judgment depends on the claim, discipline, evidence base and intended use. Use language that reflects what you actually verified:
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- “Supported by this study” when a source directly supports a bounded statement.
- “Consistent with available evidence” when multiple relevant sources point in the same direction, while leaving room for uncertainty.
- “Uncertain” when evidence is limited, mixed or still developing.
- “Not supported by the cited source” when the reference does not establish the stated point.
Do not convert an association into causation, a preliminary result into settled fact, or a limited finding into a claim about everyone. The wording should be no stronger than the evidence.
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Use tools and experts as support, not substitutes
Automated fact-checkers and search tools can help find or organize sources, but they may struggle with specialized terminology, context and unsettled findings. Treat their output as a lead to investigate, not a final authority. For a high-stakes claim or a technical question you cannot interpret confidently, ask a qualified person in the relevant field to review the source and your reading of it. The House of Commons Library recommends careful checking, ideally with an expert, while CDC author guidance stresses human review of AI output (House of Commons Library, 2026; CDC, May 28, 2026).
Human review does not transfer responsibility: if you cite or share the claim, you remain responsible for representing the evidence accurately.
Disclose AI use when research or publication requires it
If AI contributed to research or scientific writing, check the current policy of the journal, institution and funder. Requirements vary and may change. Where disclosure is required or appropriate, identify the tool and version where relevant, describe what it contributed, and state what human review or validation was performed. CDC, NIH and European Commission guidance emphasize accountability, transparency and human oversight (CDC, May 28, 2026; NIH and HHS Office of Research Integrity, May 14, 2026; European Commission, 2026).
Quick Recap
A practical claim-checking sequence
- Rewrite each compound AI statement as separate, checkable claims.
- Open each citation and confirm the document, authors or issuing body, venue and date.
- Read the relevant source material and compare its population, methods, outcome, setting and level of certainty with the claim.
- Assess the evidence’s quality, validation, publication status and limitations.
- Search for newer, independent evidence and findings that challenge the claim.
- Choose a conclusion that reflects the strength and uncertainty of the evidence; get expert review when the stakes or complexity warrant it.
- Before publishing or sharing, check any applicable AI-disclosure policy and take responsibility for the final wording.
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