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Yes—AI can help you find language problems, inconsistent terminology, mismatched numbers, missing citations, and reporting omissions before you submit a research manuscript. Use it as a documented second-pass checker, not as an editor that silently rewrites the science. You remain responsible for every claim, number, quotation, table, figure, reference, and interpretation.
Before uploading any text, check the target journal’s rules on AI, confidentiality, disclosure, and data handling. Then work from a frozen human-authored copy, run narrow checks one at a time, verify every flag against your sources and data, and record what you accepted or rejected.
What AI can and cannot check in a manuscript
A language model is useful for surfacing issues that a tired author may miss during a final read. It can compare passages, identify patterns, and turn a long document into a queue of questions for human review. Its output is not proof that an error exists, and a clean-looking response is not proof that the manuscript is correct.
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- Grammar, spelling, punctuation, unclear wording, and readability.
- Inconsistent names, abbreviations, units, capitalization, and verb tense.
- Apparent contradictions between the abstract, methods, results, tables, figures, supplementary material, and conclusion.
- Arithmetic or transcription discrepancies that should be recomputed independently.
- Claims that appear to lack citations and references cited in the text but absent from the bibliography.
- Missing items in a study-design reporting checklist or in the journal’s submission requirements.
- Possible copied phrasing, unattributed quotations, or unsupported claims that need a recognized similarity-checking process and human judgment.
- Broken cross-references, inconsistent table and figure labels, and formatting patterns that are easy to overlook manually.
Questions AI cannot certify
- Whether a result is true, clinically meaningful, or statistically valid.
- Whether a cited paper actually supports the precise claim you make.
- Whether a quotation is exact or correctly attributed.
- Whether a calculation is correct without checking the source data or a trusted script.
- Whether a manuscript is free of plagiarism or other publication-ethics problems.
- Whether a proposed rewrite preserves the intended scientific meaning.
International Committee of Medical Journal Editors (ICMJE) guidance says that authors must check the accuracy and validity of their content before submission. Treat every AI finding as a hypothesis to investigate, not as an automatic correction.
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Protect the manuscript before using an AI service
Read the target journal’s policy first
Journal rules differ. Look for instructions covering AI-assisted writing, confidentiality, retention, training use, disclosure, acceptable tools, and where a statement must appear. A journal may permit language polishing but require disclosure, prohibit uploading peer-review material, or restrict use of a service that cannot guarantee confidentiality.
ICMJE describes submitted manuscripts as privileged communications. Its guidance warns that using an AI system in processing or evaluating a manuscript may violate confidentiality when confidentiality cannot be assured without permission. The same principle matters to authors: do not send an unpublished paper, participant information, proprietary data, or material covered by a contract to a service whose handling you cannot verify.
Check the service’s data controls
Before an upload, establish what happens to the text:
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- Is the content retained, and for how long?
- Is it used to train or improve a model?
- Who can access prompts, files, and generated output?
- Can you disable retention or training use?
- Can you export the review and permanently delete the manuscript?
- Does the service store data in a jurisdiction or environment your institution permits?
- Are tables, equations, references, and attached files handled under the same terms as plain text?
If you cannot obtain clear answers, use a locally controlled or institutionally approved system, redact identifying and confidential material, or perform the review without uploading the manuscript. Redaction is not a substitute for a policy check: rare combinations of details can still identify a participant or project.
Preserve a human-authored baseline
Make a read-only copy of the exact draft you intend to check. Keep the data dictionary, analysis scripts, figures, tables, reference library, reporting checklist, and any supplementary files alongside it. Record the manuscript version and date. Do not let an AI tool overwrite this baseline; work on a duplicate or review suggestions in a separate document.
Run narrow, evidence-seeking review passes
One broad request such as “fix my science” invites silent rewriting, invented explanations, and changes that are difficult to audit. Give the system one class of check at a time and require a traceable result. A safe response format asks for the exact location, a quotation, the reason for concern, the smallest proposed change, and an uncertainty label.
1. Language and readability
Supply a section or a manageable block, not an entire confidential manuscript by default. Ask for issues rather than an automatic rewrite.
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2. Terminology and style consistency
Ask the model to build a list of terms it actually finds, rather than to impose a generic style. Include the manuscript’s preferred spelling, abbreviation rules, units, and tense conventions in your instructions.
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Find inconsistent names, abbreviations, units, capitalization, symbols, and verb tense in this section. Quote each occurrence, show the variants, and suggest one consistent form. Do not decide that two technical terms are equivalent unless the text defines them as equivalent.Check the resulting list against your protocol and field-specific terminology. A model can mistake two related measures for synonyms or normalize a unit incorrectly.
3. Internal consistency across sections
Provide the abstract, methods, results, tables, figures, supplements, and conclusion in clearly labeled blocks when confidentiality rules allow. Ask for cross-document mismatches, not a rewritten narrative.
Compare the labeled sections for internal consistency. Report only apparent mismatches in sample size, eligibility criteria, outcomes, time points, effect estimates, confidence intervals, p-values, table or figure numbers, and conclusions. For every flag, quote both passages and explain what must be checked. Do not infer which passage is correct.Pay special attention to participant counts after exclusions, denominators in percentages, subgroup labels, primary versus secondary outcomes, and whether the conclusion uses causal language that the design cannot support.
4. Numerical and transcription checks
AI can spot a number that looks inconsistent, but it is not a substitute for recomputation. Give it permission to flag only and then recalculate in the source data, spreadsheet, statistical package, or a trusted script.
Scan these tables and results for possible arithmetic or transcription discrepancies. Quote the values and show the calculation you think should be checked. Do not recompute silently and do not declare a value correct. Mark any result that cannot be verified from the supplied information.Independently verify totals, percentages, rounding, confidence-interval endpoints, effect-size transformations, unit conversions, and consistency between displayed precision and the analysis output. Preserve the calculation or script used for the decision.
5. Citation and reference checks
Use AI to locate places that need a human source check. Never accept a generated citation, DOI, title, author list, quotation, or claim of what a paper found without opening the cited publication yourself.
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Identify claims that appear to require a citation and references cited in the text that do not appear in the bibliography. Quote each claim and list the cited reference exactly as written. Do not create or repair references. Mark uncertainty when the context is insufficient.Open every cited paper, confirm that it supports the exact wording, and check that the population, intervention, comparator, outcome, date, and study design match. A real paper can still be the wrong source for a particular sentence. Check the bibliography against the journal’s required style with your reference manager or a manual review.
6. Reporting-checklist and submission checks
Identify the study design and the checklist required by the journal or field, then compare the manuscript with that checklist. Ask the model to point to missing or unclear items; have an author decide whether an item is truly satisfied.
Using the supplied study-design checklist, map each item to the manuscript section and quote the supporting passage. If you cannot find support, say “not located” and explain what an author should inspect. Do not invent compliance or rewrite the methods.Run a separate pass for the journal’s instructions: word limits, abstract headings, required reporting forms, ethics statements, funding and conflict disclosures, data-availability language, supplementary files, file naming, and figure resolution or format.
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7. Integrity signals for human follow-up
Ask for possible copied phrasing, unattributed quotations, or claims that appear unsupported, but do not treat the output as a plagiarism verdict. Follow up with a recognized similarity-checking process, inspect the original sources, and use human judgment about common technical language, legitimate quotation, and attribution.
Flag passages that may require an originality or attribution check. Quote the passage, explain why it warrants review, and distinguish common technical phrasing from a possible distinctive quotation. Do not assign a plagiarism percentage or accuse an author.Design prompts that preserve scientific meaning
Good prompts constrain the task and the allowed action. State the document’s role, the review boundary, and what the model must not change. Require evidence for every suggestion and an explicit uncertainty label.
| Prompt control | Why it matters | What to require |
|---|---|---|
| Scope | Prevents an unbounded rewrite | Name one section, table, check type, or comparison. |
| Evidence | Makes a flag auditable | Quote the exact passage and identify its location. |
| Minimality | Protects the author’s meaning | Suggest the smallest safe change; do not alter results or interpretation. |
| Uncertainty | Separates observations from guesses | Use high, medium, or low confidence and explain why. |
| Negative instructions | Blocks common failure modes | Do not invent sources, numbers, quotations, methods, or compliance. |
| Output structure | Speeds human review | Return location, quotation, concern, proposed action, and verification needed. |
For long manuscripts, work section by section and keep labels consistent. If context is split across prompts, include stable identifiers for sections, tables, figures, and versions so that a later suggestion can be traced to the correct passage.
Verify every flag instead of accepting it
Create an audit table outside the AI tool. A practical record has columns for manuscript location, the AI’s quoted flag, the source or calculation checked, your decision, the person who reviewed it, and the date. Store the original output with the manuscript version that produced it.
- Locate the quoted passage in the frozen baseline and confirm that the model did not misquote it.
- Check the issue against the protocol, raw or processed data, analysis code, original literature, reporting checklist, and journal instructions as appropriate.
- Decide whether the flag is correct, incorrect, or unresolved. Record the reason rather than simply accepting or deleting it.
- If you edit the manuscript, compare the changed version with the baseline and inspect nearby sentences for altered meaning.
- Ask a coauthor or subject-matter colleague to review important claims, methods, and interpretation; language polishing cannot replace scientific review.
When a model proposes a numerical correction, reproduce the calculation independently. When it proposes a citation change, read the source. When it proposes stronger or weaker language, compare the wording with the study design and prespecified analysis. Do not allow an AI response to become the only evidence for a change.
Complete a human final sign-off
After all edits, read the clean manuscript from start to finish without relying on the AI’s issue list. Then inspect the high-risk elements directly:
- Abstract-to-main-text agreement.
- Every number, denominator, unit, decimal place, confidence interval, and p-value.
- Table and figure labels, legends, captions, cross-references, and supplementary files.
- Reference accuracy, quotations, and the relationship between each important claim and its source.
- Ethics approval, consent, funding, conflict-of-interest, author-contribution, and data-availability statements.
- Required reporting forms, journal word limits, file names, and submission metadata.
Use the final manuscript version to generate a clean comparison against the frozen baseline. Resolve every unresolved flag or explicitly document why it remains unresolved. The corresponding author and coauthors—not the tool—make the final scientific and ethical sign-off.
Disclose AI use accurately
Follow the target journal’s exact wording and placement requirements. ICMJE says authors should disclose at submission whether AI-assisted technologies were used and describe the tool and purpose in the cover letter and submitted work where applicable. Writing assistance may be described in acknowledgments; use for data collection, analysis, or figure generation should be described in the Methods. A journal may have more specific instructions, so its policy controls.
Keep a private record of the tool or service, date and version when available, the purpose of each pass, the material supplied, and the decisions made by authors. Do not claim that AI performed a validation it did not perform, and do not hide substantive AI involvement behind a vague statement about proofreading.
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AI systems must not be listed as authors. ICMJE states that chatbots and other AI-assisted tools cannot be responsible for the accuracy, integrity, and originality of the work. The Committee on Publication Ethics (COPE) likewise says that AI tools cannot meet authorship requirements and that authors remain fully responsible for content produced with AI, including potential publication-ethics breaches. COPE’s position was last reviewed or changed on 20 August 2024; ICMJE’s recommendations page states an update in January 2026.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a workflow or tool using risk-based criteria
The most capable writing interface is not automatically the right choice for an unpublished manuscript. Compare the entire handling process, including human review and record keeping.
| Criterion | Questions to answer before use |
|---|---|
| Confidentiality | Can the provider and your institution guarantee appropriate protection for an unpublished manuscript and any participant information? |
| Retention and training | How long are prompts and files kept, and are they used to train or improve models? |
| Access and location | Who can view the material, where is it stored, and does that meet institutional or contractual requirements? |
| Journal or institutional approval | Does the target journal, funder, employer, or ethics agreement permit this workflow? |
| Citation transparency | Can the system show exactly which supplied text it used, and can you independently verify every source? |
| Meaning preservation | Can you review a side-by-side diff and prevent silent changes to numbers, qualifiers, methods, or interpretation? |
| Traceability | Are prompts, outputs, document versions, and accepted or rejected edits exportable? |
| Tables, equations, and references | Does the workflow preserve structure, symbols, footnotes, and reference fields rather than flattening them? |
| Export and deletion | Can you retrieve a complete audit record and delete uploaded material when the review is over? |
| Cost and availability | Is the option available to all authors who must review it, without a plan or access change interrupting the audit trail? |
| Human editorial review | Is there a qualified person who will decide whether a flagged issue is real and scientifically important? |
A locally controlled or institutionally approved system may reduce confidentiality risk, but it does not remove the need for verification or disclosure. A commercial service with strong controls may still be unsuitable if the journal prohibits its use. Decide from the documented terms and the manuscript’s sensitivity, not from marketing claims or a model’s confidence.
Common failure modes and safer fixes
Uploading first and checking policy later
Why it fails: The manuscript may already have been retained, exposed to unauthorized access, or processed in a way the journal or institution forbids.
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Safer fix: Check journal, institutional, contractual, and ethics requirements before any upload. If handling cannot be established, use an approved local option or do the review without uploading.
Requesting a complete rewrite
Why it fails: Broad rewriting can change causal language, eligibility criteria, statistical qualifiers, or the meaning of a negative result while leaving no clear audit trail.
Safer fix: Run one narrow pass at a time, require quoted evidence and minimal edits, and compare every accepted change with the baseline.
Trusting a polished or confident answer
Why it fails: AI can produce incorrect, incomplete, or biased output, including plausible explanations for a non-existent problem.
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Accepting generated references
Why it fails: A citation can be fabricated, misquoted, or real but irrelevant to the claim.
Safer fix: Never accept an AI-generated reference without opening the original publication and checking the exact support, bibliographic details, and context.
Letting AI “check” arithmetic
Why it fails: A model may reproduce a transcription error or calculate from incomplete context.
Safer fix: Use the model only to flag discrepancies, then recompute from source data or a trusted script and retain the calculation.
Assuming a similarity flag proves plagiarism
Why it fails: Technical phrases, standard methods, and legitimate quotations can resemble published text, while a model may miss a meaningful overlap.
Safer fix: Use a recognized similarity-checking process, inspect sources, verify attribution, and make the ethics judgment yourself.
Failing to preserve versions
Why it fails: Without the original draft and dated outputs, you cannot tell what changed, why it changed, or whether a later edit introduced an error.
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Listing the tool as an author
Why it fails: An AI system cannot take responsibility for accuracy, integrity, originality, or publication-ethics obligations.
Safer fix: Disclose the tool and purpose where the journal requires it, credit human authors for all content decisions, and never assign authorship to the system.
When to stop using AI for the manuscript
Stop and move to a human or approved local workflow if the service’s retention or training terms are unclear, if the journal forbids the upload, if the material contains identifiable or proprietary information that cannot be protected, or if repeated suggestions alter scientific meaning. Also stop when you cannot reconstruct which version produced an output or verify a flagged claim from the underlying evidence.
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The reliable outcome is not an AI-certified manuscript. It is a manuscript whose authors have used a bounded second-pass check, investigated each signal, preserved an audit trail, followed the journal’s policy, and personally signed off on the final scientific content.
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