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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesYou generally cannot prove that a scientific paper’s text or figures were generated by AI just by looking at them. Treat odd wording, polished prose, or a suspicious-looking image as a reason to verify—not as evidence of AI use. Check disclosure and journal policy, trace references and claims to their sources, and assess figures for specific integrity concerns. AI-assisted writing, AI-generated or altered imagery, and conventional image duplication are different issues and need different checks.
What can—and cannot—show that AI was used
There is no universal writing-style or visual clue established here as proof that AI produced scientific text or an image. A fluent paragraph may contain errors; awkward phrasing does not establish AI use. Likewise, an unusual-looking figure may merit scrutiny without showing how it was made or whether anyone acted improperly.
Keep three questions separate:
- AI assistance with writing: Was an AI tool used to draft, edit, translate, or otherwise assist with text, and was that use disclosed as required?
- AI-generated or altered imagery: Was an image created or changed with AI, and does the journal permit that use for this image’s purpose?
- Image duplication or manipulation: Do parts of a figure appear duplicated or altered in a way that could obscure or introduce scientific information? This can be an integrity concern regardless of whether AI was involved.
Evidence for one does not establish the others. In particular, a method designed to find duplicated images is not automatically an AI-image detector.
How to check a paper systematically
1. Look for a disclosure, then check the journal’s rules
Review the acknowledgements, methods, figure captions, and any other section named in the journal’s author instructions. A useful disclosure identifies the tool and describes its role. It records what the authors say they did; it does not independently establish that the content, data, or analysis is accurate.
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Requirements vary by journal and by use. The ICMJE recommendations address disclosure and author accountability. The CDC’s scientific-work guidance includes disclosure considerations and advises authors to consult current journal rules. Elsevier’s journal policy distinguishes explanatory images from research data images and formal research methods. JAMA Network’s author guidance sets requirements within its own publications. Consult the current instructions for the specific journal rather than applying one publisher’s policy to all papers.
2. Verify the references and what they support
Search each important citation in an authoritative record, such as the journal or publisher page or a DOI record. Check that the work exists and that its title, authors, journal, and year match the reference. Then read enough of the cited source to determine whether it actually supports the attached claim; a real citation can still be misrepresented.
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This matters because plausible-looking references are not necessarily real. NIH-supported research-integrity guidance warns that presenting AI-generated, non-existent references as real may constitute fabrication. ICMJE and Elsevier also place responsibility on humans to check attribution and AI-generated references: see the NIH guidance and Elsevier’s book and commissioned-content policy.
3. Trace important claims to the underlying evidence
Check consequential quotations, numerical results, methods, and conclusions against their cited sources. Confirm that a quoted passage is accurate, that a figure or statistic is reported in the cited work, and that the conclusion does not claim more than the evidence supports. ICMJE cautions that AI output can be incorrect, incomplete, or biased; the CDC similarly tells authors to verify sources and extracted data. Authoritative-sounding prose is not a substitute for checking.
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4. Inspect figures for duplication or unexplained alteration
Compare panels and regions within the paper. If source records or earlier publications are available, compare those too. Note specific concerns—such as apparently repeated panels presented as different experiments or changes that could obscure or introduce scientific information—rather than relying on a vague impression that an image “looks like AI.” Such observations warrant expert review; they do not by themselves prove AI generation, misconduct, or intent.
A 2021 study by Mazaheri, Urrutia Avila, and Roy-Chowdhury reported 90% accuracy for its method on the study’s evaluated task of detecting duplicated images in scientific publications. The authors also reported an approximately 13% improvement in detection accuracy over comparison manipulation detectors in that evaluation. Those are study-specific results for image duplication—not accuracy figures for detecting generative AI, nor guarantees for other images or settings. See the study.
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5. If concerns remain, report the specifics through the right channel
Record the exact passage, reference, or figure panel and explain what you could not verify. If the issue is substantial, use the journal’s contact or research-integrity process, or contact the relevant institutional office where appropriate. Do not publicly accuse authors solely because an automated detector or visual impression raised suspicion. A flag is a prompt for human review, not a verdict.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why AI detectors are not a shortcut to a verdict
Different checks answer different questions: a disclosure describes declared tool use; reference verification checks whether sources exist and support claims; figure comparison can identify possible duplication or alteration. These should not be conflated with a general-purpose claim that a detector can determine whether a paper was written or illustrated by AI.
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The sources cited here do not provide a comparable current benchmark for AI-text detectors, so they do not support ranking named detectors or assigning a general accuracy rate. Any tool result should be interpreted in light of what it was designed and evaluated to detect, the evidence it examines, and whether a person reviews the flag. Also consider confidentiality before uploading an unpublished manuscript or image to an external service.
Who is accountable for AI-assisted content?
Human authors remain responsible for submitted work, including checking AI-assisted text, verifying sources, and providing appropriate attribution and disclosure. ICMJE advises authors to review and edit AI-generated content because it can be incorrect, incomplete, or biased. The CDC gives a similar warning about authoritative-sounding output. A disclosure is useful context, but responsibility for the paper’s accuracy does not move to the tool.
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