MLR review stands for Medical, Legal and Regulatory review — the formal process by which a pharmaceutical or biotech company vets promotional and scientific content before it reaches a healthcare professional or a patient. Nothing goes out without it, and in most organisations a cycle runs in weeks rather than days.
What the three letters actually check
Each reviewer is looking for a different failure, which is why the process is cross-functional rather than sequential sign-off by one person.
- Medical. Is every claim supported by the referenced data, is the science represented fairly, and is the balance between benefit and risk accurate?
- Legal. Does the material create liability — comparative claims, off-label implication, intellectual property, contractual exposure?
- Regulatory. Does it comply with the approved label and the applicable codes and regulator expectations in each market it will appear in?
A piece can pass two and fail the third, which is the usual reason for a second and third cycle.
Why it takes weeks
Rarely because reviewers are slow. The time goes into four places that have nothing to do with reading.
- Queueing. Reviewers are senior people with other jobs. Material waits.
- Round trips. A comment on slide four triggers a change on slide eleven, and the whole asset re-enters review.
- Reference checking. Verifying that each claim maps to the cited source is mechanical, slow and where most of the corrections land.
- Version confusion. Which draft is under review, and which comments belong to which version — very often tracked in a spreadsheet.
Only the first of those is a capacity problem. The other three are handling problems, which is precisely why AI has an opening here.
Where AI genuinely helps
Veeva reports up to a 75% reduction in MLR cycle time with AI-supported review, and Falcon MLR — its agentic compliance layer built on the Copli acquisition and integrated with PromoMats and Veeva MedComms — targets a 70% reduction in manual MLR labour over five years. See Veeva’s own material on AI-supported MLR.
The useful reading of those numbers is which tasks they come from:
- Pre-review checks. Catching the obvious failures before a reviewer ever opens the file, so cycles are spent on judgement instead of on formatting and missing references.
- Reference verification. Matching each claim to its cited source. Mechanical, exhaustive, and unpleasant for a human to do at volume.
- Content reuse. Recognising that a claim has already been approved elsewhere and does not need re-litigating.
- Queue and version state. Knowing what is where — the part still living in a spreadsheet in most organisations, where roughly 88% of spreadsheets contain errors.
The caveat that matters
Every published figure assumes the operating model changes too. Vendors building around content reuse, pre-review checks and tiered workflows report large cycle-time cuts precisely because the workflow was redesigned, not because a model was added to it.
AI applied to an unchanged review process mostly makes the existing bottleneck arrive sooner. If reviewers are the constraint and material simply reaches them faster, you have built a faster queue.
Where to start, if you are not on a platform
Not every organisation running MLR has Veeva. If you do not, the first move is not to buy a platform — it is to fix the thing that decides whether anything is on time.
Start with queue and version state: what is in review, with whom, since when, against which version. That is process metadata, so no unpublished scientific content leaves your environment — which matters, because Wiley, Wolters Kluwer and Elsevier all warn against putting unpublished material into general-purpose LLMs. It is the cheapest, lowest-risk and most visible starting point, and it builds the audit trail you will want before anything touches content.
We have set out the wider picture in AI for medical publishing and compared the tooling by job — including PromoMats, Quick Check and Falcon MLR — in AI tools for medical publishing.
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