MLR-with-AI Review Checklist - Medical Writing AI Playbook

A structured MLR review checklist with AI-specific items for content where AI was used in any drafting, editing, verification, or generation step. Extends a standard MLR checklist; does not replace it. For the principles behind this checklist, see Review and Accountability, Source Grounding, and Declaring AI Use.

How to use this checklist

Run alongside your standard MLR checklist, not in place of it. The AI-specific items are additive.
Require every AI item to clear before sign-off. A “skipped because it’s AI” item is the failure mode this checklist exists to prevent.
Match scrutiny to the risk tier. Critical and high-risk content gets every item; lower-risk content can be more selective. Use the risk-tier framework to calibrate.
Review the audit log before starting. Without it, the AI-specific checks become best-guess.

Before the review

The AI audit-trail log for this deliverable is complete and accessible
All cited references are accessible as full text (not abstracts)
The approved messaging framework or claims matrix is to hand
The applicable code is identified (ABPI, IFPMA, FDA OPDP, etc.)
Risk tier of the deliverable is documented
Disclosure language for the deliverable is drafted (see Disclosure Language Template)

AI-specific checks

The items most likely to fail review on AI-assisted content. Run these first.

Reference and source integrity

No fabricated references. Every cited reference exists, has a working DOI/PMID, and supports the claim it’s cited for. If closed-loop verification was run (e.g., RefCheckr), confirm the output was reviewed and accepted, not rubber-stamped.
No fabricated data. Numbers, percentages, p-values, hazard ratios in the deliverable match the source data exactly. Spot-check at least 20% manually — automated checks miss subtle mismatches.
No merged findings. AI sometimes blends results from different studies. Confirm each claim is attributed to the correct study, population, and analysis type.
No drifted qualifiers. Subgroup, post-hoc, exploratory, and secondary-endpoint markers present in the source are preserved in the deliverable.

Claim integrity

Claims fall within the approved messaging framework. AI can drift toward stronger language; verify each claim against the framework before sign-off.
No off-label implications. Read each claim against the approved indication. AI is prone to subtle scope expansion (“can help with X” when only X in a narrow population is approved).
Comparative claims are substantiated. AI sometimes implies superiority where the trial was designed for non-inferiority. Confirm comparative claims have head-to-head substantiation.
Language strength matches evidence strength. “Significantly reduces” requires statistical significance in the source. AI rewrites can soften or strengthen language without adjusting the cited evidence.

Safety and balance

Fair balance is maintained. AI-drafted content can over-emphasise efficacy. Safety information must be present, prominent, and proportionate.
Safety claims verified with the same rigour as efficacy claims. AI sometimes treats safety information as boilerplate. Verify against the SmPC or USPI.
No minimised limitations. Limitations of the evidence are visible, not buried.

Compliance and documentation

Code-specific signals checked. ABPI Code, IFPMA Code, or relevant national code obligations have been pre-screened (e.g., via MedCheckr).
Disclosure language is present. The deliverable includes the AI-use disclosure appropriate for its venue (manuscript, abstract, regulatory submission, internal report).
AI use is consistent with sponsor SOP. Some sponsors restrict AI use for certain deliverable types — confirm compatibility before sign-off.
Audit log is contemporaneous. Entries dated alongside the work, not reconstructed at submission.

AI-generated visuals

AI-generated figures are not presented as primary scientific data. Concept visuals, visual abstracts, and illustrations are clearly labelled as conceptual.
Patient depictions don’t imply outcomes. AI image tools can render scenes that suggest treatment results — check for unintended implications.
No identifiable real people or recognisable products unless explicitly licensed.

Standard MLR checks (with an AI lens)

These exist on every MLR checklist; the notes flag where AI-assisted content needs different attention.

All claims substantiated. Same standard as for human-written content; verify against cited evidence.
References numbered, formatted, and accessible. AI-fabricated references will trip here if missed earlier.
Prescribing information present and current. Confirm no AI-hallucinated PI content.
Approval workflow followed. AI-assisted content used the standard review path, not a shortcut.

Sign-off block

Field	Value
Deliverable version reviewed	[Version / file name]
All AI-specific items cleared	[Yes / No]
All standard MLR items cleared	[Yes / No]
Audit log reviewed	[Yes / No]
Disclosure language confirmed	[Yes / No]
Reviewer name and role	[Name, role]
Sign-off date	[YYYY-MM-DD]

Common failure patterns

Treating AI assistance as a polish layer

Reviewers sometimes apply less scrutiny to “AI-edited” content than to “AI-drafted” content, on the assumption editing is low-risk. AI editing can rephrase claims in ways that subtly drift from the source. Apply the same checks regardless of stated scope.

Skipping the audit-log review

The audit log answers “what did the AI actually do?” Without reviewing it, the AI-specific checks become best-guess. Always review the log before sign-off.

Over-trusting closed-loop output

Closed-loop tools like RefCheckr and MedCheckr reduce drift; they don’t eliminate it. A “passed” closed-loop output still needs human verification on the high-stakes items.

Defaulting to the standard checklist for AI-assisted content

The AI-specific items are not optional adjuncts. They cover failure modes that don’t appear in standard checklists. Skipping them is a common pathway to desk rejection or, in promotional contexts, to MLR rework.

Reviewing without full source access

The reviewer has the abstract but not the full paper. They cannot verify a subgroup result. They pass it. AI-assisted content amplifies this risk because plausible-sounding claims about subgroups are common AI outputs. Confirm full-text access before review.

Review and Accountability — the principle this checklist operationalises
Source Grounding — the underlying claim-to-source principle
Declaring AI Use — the disclosure principle this checklist confirms
AI Failure Modes — the failure patterns this checklist guards against
AI Audit-Trail Log Template — feeds the “before the review” prep
Disclosure Language Template — pairs with the disclosure-confirmed item
Check Promotional Compliance — workflow this checklist supports
Final Human Review — broader review workflow for any AI-assisted deliverable

Last reviewed: 4 May 2026 · 7 min read

AI Audit-Trail Log Template Pre-submission QC Checklist