Claims are at the center of pharmaceutical promotional review.
Before a promotional statement can be approved, reviewers need to understand what is being claimed, whether appropriate evidence supports it, and whether the presentation is consistent with applicable regulatory requirements.
Traditionally, much of that work begins manually.
Artificial intelligence is changing that.
What Is AI Claims Detection?
AI claims detection uses artificial intelligence to analyze promotional content and identify language that may constitute a claim requiring review or substantiation.
This can include statements related to efficacy, safety, product characteristics, clinical outcomes, comparisons, or other promotional messages.
Rather than relying exclusively on reviewers to locate every claim manually, AI can provide an intelligent first pass.
Moving Beyond Static Claims Libraries
One important distinction in modern AI-powered MLR is the ability to analyze content contextually.
ERMA Systems uses AI-driven claims detection rather than depending exclusively on a static database of predetermined claims.
This matters because pharmaceutical promotional content constantly changes.
New campaigns, indications, studies, messages, and materials introduce language that may never have appeared in an organization's previous claims library.
AI can help identify those potential claims dynamically.
Claims Are Only the Beginning
Identifying a claim does not answer the most important question:
Can we support it?
This is where agentic MLR becomes particularly powerful.
Once a potential claim has been identified, specialized AI capabilities can help evaluate available references and locate evidence potentially relevant to that statement.
The workflow begins connecting three critical components:
Claim → Evidence → Regulatory Context
Instead of treating those as independent manual activities, an agentic system can help analyze their relationship.
Improving the Reviewer Experience
The goal isn't to have AI make the final regulatory decision.
The goal is to give reviewers a better starting point.
Rather than opening a promotional asset and beginning analysis from scratch, reviewers can enter the process with potential claims already identified and relevant information surfaced for consideration.
That allows highly trained professionals to spend more time evaluating complex questions and less time performing repetitive investigative work.
Building Intelligent Promotional Review
Claims detection demonstrates the broader potential of AI in pharmaceutical MLR.
Once software can understand what is being communicated inside promotional content, it can begin assisting with increasingly sophisticated review activities.
ERMA Systems is building toward that environment through an agentic AI architecture designed specifically for life sciences compliance.
MLR software has historically managed documents.
The next generation will increasingly understand them.