Agentic MLR Is Here. But AI Is Only as Good as the Evidence Behind It.
For years, the life sciences industry has talked about using artificial intelligence to make Medical, Legal and Regulatory (MLR) review faster.
That conversation is changing.
We are moving beyond AI that simply assists reviewers toward agentic AI—technology designed to perform portions of the review itself, identify potential compliance issues, connect claims with substantiation and determine where human attention is actually required.
The shift raises a fundamental question: What has to be true before we can trust AI to automate MLR review?
The Real Challenge Isn't Finding the Claim
AI is becoming remarkably capable of identifying language that constitutes a claim. It can recognize that two statements communicate essentially the same message even when the wording is different. It can search large bodies of information, identify potential inconsistencies and flag content that warrants review.
Those capabilities are valuable. But identifying a claim is only the beginning.
The harder questions are: What evidence supports it? Where in that evidence is the substantiation? Is the reference still current and approved? Does the evidence support the precise way the claim is being communicated? Is the claim consistent with the product labeling? Has the approved claim changed since the last time this content was used?
Those questions cannot reliably be answered simply by pointing a generative AI model at thousands of documents and asking it to make a judgment. They require structure.
The Claims Library Is Becoming Infrastructure
Historically, a claims library could be viewed primarily as a repository—a place to store approved claims and their associated references.
In an AI-enabled MLR environment, the claims library becomes part of the evidence architecture behind the review process. Each claim can be connected to its approved language, supporting reference, specific substantiating data or anchor, labeling, indication, audience, geography and approval status.
Instead of merely documenting what has already been approved, claims libraries become the foundation that allows technology to determine what is already known—and what is genuinely new.
From Document Review to Exception Review
Today, much of MLR still operates at the document level. A new asset enters review and humans inspect the content, locate supporting references, evaluate claims, verify required information and identify deviations.
But imagine a different model. The system already understands the approved claims and their substantiation. It recognizes where those claims appear in new content—even when they have been rephrased. It knows which references are approved, which claims have changed and which statements do not match previously approved evidence.
The review process can then begin to shift from:
Review everything
to:
Review what changed.
That is the real promise of agentic MLR—not replacing medical, legal or regulatory judgment, but removing the work that does not require judgment.
Human Oversight Becomes More Important, Not Less
The more work AI performs, the more important it becomes to be clear about which decisions should remain human.
Scientific interpretation is not always binary. A reference may contain the words supporting a statement while the overall communication still overstates the evidence. A claim may be technically accurate but misleading in context. New clinical data may require interpretation that has never previously been encoded into an approved claim.
These are judgment calls. The objective should therefore not be autonomous MLR. It should be intelligent division of labor: machines excel at searching, comparing, checking, matching and identifying deviations at scale, while humans excel at interpretation, context, risk assessment and judgment.
The Next MLR Transformation Starts Before MLR
If claims are structured when they are approved, evidence is properly anchored, references are connected and new communications are checked against that foundation before submission, MLR no longer has to rediscover the same information every time an asset enters review.
AI then becomes more than a tool for reviewing documents faster. It becomes part of a continuous evidence system. Agentic MLR is an important development, but the technology that ultimately transforms review may not be the AI agent itself. It may be the structured, traceable evidence that gives the agent something trustworthy to reason against.
Want to see how disciplined your claims management process is today? Take our free Claims Management: MLR Bottleneck Assessment to find out where your process stands and where a governed claims library could strengthen it.