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Can AI help with MLR review without creating compliance risk?
Where does the time in MLR review actually go?
Anyone who has sat between a brand team and its medical, legal and regulatory (MLR) reviewers knows that the reading is rarely the slow part. The time goes into everything around it:
- Assembling the comments. Feedback arrives as annotations in a review system, as an emailed list, as a marked-up PDF, or as notes from a meeting.
- Reconciling reviewers. Medical asks for one change to a line, Legal asks for a different one, and someone has to notice they are talking about the same sentence.
- Tracking rounds. Version three has to answer every comment on version two, and a reviewer who reverses their own round-one comment has to be caught, not silently obeyed.
- Chasing people. A round stalls because a reviewer is late, or because nobody was sure who was submitting it.
- Writing the response. The comment-by-comment response document that reviewers expect is assembled by hand.
None of that work requires a regulatory judgment. All of it is where AI can help.
What can AI safely do in a review round?
Used well, AI acts as the coordinator's second pair of hands:
- Put every comment in document order, beside the line it refers to, whatever channel it came in on.
- Mark lines that two reviewers commented on so they are read together, not answered one at a time.
- Show the approved claim nearest to each comment. When a reviewer questions a line, the coordinator should see at once whether that line carries approved wording and what the approved version says.
- Suggest a call for each comment: accept, accept with a change, decline with a reason, ask the reviewer for clarity, or route it to Legal or Regulatory. The coordinator chooses, every time.
- Draft the reply to the reviewer and the response table from the calls the coordinator actually made.
- Keep the history of each round, so a resubmission carries its answers forward and a reversal is visible.
Each of those steps either saves reading time or prevents a missed comment. None of them decides anything.
What must AI never do in MLR review?
Four lines keep AI on the right side of the review:
- It never approves. The highest state machine-made work should reach is ready for human review. Approval is a recorded act by a named reviewer.
- It never answers a legal or regulatory question itself. When a comment needs a legal or regulatory judgment, the right behavior is to mark it as needing that call, keep it open until a person answers, and draft the question for the reviewer.
- It never chooses between two reviewers. When Medical and Legal disagree on a line, AI can propose wording that satisfies both where that is possible and say plainly where it is not. The decision stays with the people accountable for it.
- It never sends on anyone's behalf. Follow-ups to a slow reviewer can be drafted, but the coordinator sends them, from their own email, after reading the exact text.
How should AI handle a change to approved wording?
This is the case most likely to create risk, and the easiest to get wrong. A reviewer's suggested edit can look harmless and still move a line away from wording that was already approved, for example by dropping a qualifier or widening the population.
The safe pattern is to flag it at the comment: accepting this departs from approved wording. The coordinator can still accept it, but knowingly, and the change goes back through review rather than slipping through as a copy edit.
Whatever does the flagging, word overlap is a poor guide to risk: a sentence that loses its qualifier often looks more like the approved original, not less. We explain why in Similarity is not compliance.
Can AI catch problems before the review starts?
It can, and this is where many avoidable rounds can be prevented. If every draft is built from confirmed messages that carry their own proof points and do-not-say lines, a draft that claims more than the evidence supports can be stopped before a reviewer reads it. Reviewers then spend their time on judgment, not on catching sentences that should never have been written.
The rule that makes this work: an adapted version may say less than the approved message, but never more. A dropped statistic is a normal adaptation; a new comparison or a broader population is a new claim, and new claims go to people. How do you keep one approved message true across audiences and languages? explains the checking in detail.
What should you ask an AI vendor about MLR?
| Ask | A good answer |
|---|---|
| Can your AI mark anything as approved? | No. It stops at ready for review; approval is a named person's recorded act |
| What happens to a comment that needs a legal judgment? | It is marked as needing that call and stays open until a person answers |
| How do you handle two reviewers who disagree? | Shown together; wording proposed where both can be satisfied; the decision stays with people |
| How do you spot a change to approved wording? | Flagged at the comment when the line carries approved wording, so the change goes back through review |
| Does anything get sent automatically? | No. Drafts are sent by the coordinator from their own email |
| Where is the record? | Every call, every round and who made it, in a record that cannot be quietly edited |
How does CommsRx approach it?
In CommsRx, review happens in the Review Room. It shows where the round stands in one sentence, the reviewers and their status, every comment in document order with lines marked "read together," the nearest approved claim beside each comment, Ally's suggested call with a draft reply, and the response table built from the coordinator's own calls. Ally never answers a legal or regulatory question herself, and CommsRx sends nothing: a follow-up opens in the coordinator's own email, ready to read and send.
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About this page
Drafted with AI assistance from CommsRx's own product records, then edited and checked by Abhi Basu, founder of CommsRx and Signal Fidelity Group, who has spent more than two decades in pharma communications, including as the intermediary between brand teams and their MLR reviewers. Last reviewed October 19, 2026. Corrections: help@signalfidelitygroup.com.