Pre-submission QC Checklist - Medical Writing AI Playbook

A final-gate quality-control checklist for AI-assisted deliverables before they leave your desk. Covers the categories major publishers and regulators now screen for automatically — reference integrity, image integrity, statistical accuracy, AI disclosure, methodology compliance, plagiarism. Catching these yourself is faster than the round-trip from a desk rejection. For the principle behind this checklist, see AI in Peer Review. Use after Final Human Review, as the last step before submission.

How to use this checklist

Run on the final, submission-ready version — not on a working draft.
Run yourself; don’t outsource the gate. The reviewer signing off is accountable for what passes through.
Match scrutiny to the venue. A high-impact journal submission gets every item; an internal report needs only the relevant subset.
Document outcomes — the AI Audit-Trail Log Template supports the trace from “checked” to “actually verified”.
When something fails, fix it before submitting. “We’ll catch it in revisions” is the cycle this checklist is designed to break.

Reference integrity

The single most-detected failure mode on AI-assisted submissions.

Every cited reference exists. DOI or PMID resolves to the actual paper.
Claims are supported by the cited source. Closed-loop verification (e.g., RefCheckr) was run and outputs reviewed.
No “borrowed” findings — claims attributed to the wrong study, population, or analysis type.
Subgroup, post-hoc, exploratory qualifiers preserved where present in the source.
Self-citation is appropriate and not over-used.

Data and statistical accuracy

Numbers in the deliverable match the source data exactly (p-values, hazard ratios, percentages, sample sizes).
Confidence intervals reported where the source reports them.
No impossible statistics (GRIM violations, p-values inconsistent with reported test statistics, decimal drift).
ITT vs. mITT vs. per-protocol analyses correctly labelled.
Endpoints correctly identified as primary, secondary, exploratory.

Image and figure integrity

No image duplications across this submission or prior published work.
No splicing, cropping, or contrast adjustments that could be challenged.
Image-integrity check run (e.g., Imagetwin, Proofig) for any submission with figures from human bodies, gels, blots, or composite assemblies.
AI-generated images (concept visuals, visual abstracts) are explicitly labelled as conceptual, not as primary data.
Figure captions match the figure content and the underlying source.

AI use and disclosure

AI disclosure language drafted using the Disclosure Language Template and customised to this deliverable.
Disclosure specifies tool name, version, scope, and named reviewer.
Disclosure placed in the location the journal or venue requires (Methods, Acknowledgments, dedicated AI-use statement, footer).
AI audit-trail log is complete and accessible — every AI use captured with reviewer.
No undisclosed AI use that would appear in the audit log.

Methodology and reporting compliance

Reporting guideline followed for the study type (CONSORT for trials, PRISMA for systematic reviews, STROBE for observational, etc.).
Methods section is complete enough for reproducibility.
Pre-registration referenced where applicable.
Limitations clearly stated.

Plagiarism and originality

iThenticate or equivalent similarity check run on the final draft.
No verbatim text reuse from prior published work without proper attribution.
Self-plagiarism (text reuse from author’s own prior publications) flagged where significant.
Quotation marks and citation used correctly for all directly reproduced passages.

Authorship and affiliations

All authors listed meet authorship criteria (ICMJE or journal-specific).
No “courtesy” or “gift” authorship.
Each author’s affiliation is current and accurate.
ORCID iDs included for each author.
Conflicts of interest disclosed in full.
AI is not listed as an author. (Universally rejected by major journals; immediate desk rejection.)

Venue-specific checks

Journal’s specific AI-use policy reviewed (see Declaring AI Use for links).
Word, figure, and table limits respected.
Required sections present (e.g., data availability statement, ethics approval, funding).
File formats and naming conventions match the submission system requirements.
Cover letter aligns with the submitted content.

Sign-off block

Field	Value
Deliverable version	[Version / file name]
Submission target	[Journal / regulator / sponsor]
All reference integrity items cleared	[Yes / No]
All data and statistical items cleared	[Yes / No]
All image integrity items cleared	[Yes / No]
AI disclosure and audit log complete	[Yes / No]
Methodology and reporting items cleared	[Yes / No]
Plagiarism check passed	[Yes / No]
Authorship and affiliations confirmed	[Yes / No]
Venue-specific items cleared	[Yes / No]
Final reviewer name and role	[Name, role]
Sign-off date	[YYYY-MM-DD]

Common gaps that trigger desk rejection

Fabricated references that closed-loop verification would have caught

The single most common failure on AI-assisted submissions. The reference list looks plausible, the DOIs are formatted correctly, but the papers don’t exist or don’t say what’s claimed. Always run reference verification before submission.

AI disclosure that's vague or missing

“AI tools were used” is vague enough to draw editor questions. Specific, named, scoped disclosure (with reviewer accountability) is what publishers expect. The Disclosure Language Template gives a starting point per scenario.

Image issues found by Imagetwin or Proofig

Publisher-side image-integrity tools share databases. A figure flagged elsewhere will likely flag here. Run an integrity check before submission, not after.

Statistical inconsistencies that GRIM finds

Means and standard deviations that can’t arise from the reported sample sizes. AI-drafted summaries sometimes invent or paraphrase statistics that fail this check. Verify against source data.

Authorship issues — AI listed, courtesy authors, missing ORCIDs

Universally rejected: AI as author. Increasingly checked: ORCID for each author, transparent contribution statements, no honorary authorship. Confirm before submission.

AI in Peer Review — the principle this checklist operationalises
Declaring AI Use — the disclosure principle
Source Grounding — the underlying claim-to-source principle
Disclosure Language Template — pairs with the disclosure-confirmed item
AI Audit-Trail Log Template — pairs with the audit-log-complete item
MLR-with-AI Review Checklist — for promotional content; this checklist is for publication and regulatory submissions
Verify Claims Against References — workflow that produces the reference integrity outputs
Final Human Review — workflow that precedes this gate

Last reviewed: 4 May 2026 · 7 min read

MLR-with-AI Review Checklist