Pharma marketers know the struggle: creating new content is slow because of Medical, Legal, and Regulatory (MLR) review. Every word, claim, and reference has to be checked for accuracy and compliance. This step is critical for safety and trust, but it often turns into a bottleneck. Campaigns stall for weeks—or even months—while drafts bounce back and forth.

Now, AI pre-screening tools are starting to change the game. By scanning promotional content before it reaches reviewers, these tools can flag problems early. That means faster approvals, fewer errors, and more time for teams to focus on strategy instead of back-and-forth edits.

Why MLR Review Is So Slow

The MLR review process is designed to protect patients, but it often slows down content delivery.

• Complex rules: Every claim must have an approved reference, and risks must be shown with benefits.
• Multiple reviewers: Legal, medical, and regulatory teams must each approve content.
• Heavy volume: Pharma companies create hundreds of assets across geographies and channels.
• Manual checks: Reviewers still spend hours scanning for unapproved claims, missing references, or off-label language.

This process is critical for compliance, but it means content cycles can stretch from days into weeks. In some cases, delays can hold up entire campaigns.

How AI Pre-Screening Works

AI pre-screening tools act like a filter before content hits MLR review.

Here’s how it works:

1. Content is drafted in a CMS or content hub.
2. AI runs a pre-check—scanning for unapproved claims, missing references, or risky wording.
3. Flags are raised with risk scores (low, medium, high).
4. Writers fix issues before submission, cutting down on errors MLR reviewers need to catch.

Some tools also compare language against a claims library (a list of pre-approved statements) and even suggest compliant wording alternatives.

The result: content that is cleaner, safer, and faster to approve.

Real Benefits Pharma Is Seeing

Early use of AI in MLR review is already showing clear gains.

• Faster approvals: Some companies report review cycles shortened by 2–3x because drafts arrive with fewer errors (Anthill).
• Lower reviewer workload: Medical and legal teams spend time on true risks, not minor errors.
• Consistency across markets: AI helps apply the same rules globally, even when multiple affiliates or agencies are creating content.
• Better compliance: Catching problems early reduces the chance of risky content slipping through.

👉 Want to see how AI pre-screening could cut your approval cycles in half? CI Life helps pharma companies set up compliance automation, build claims libraries, and integrate AI into review workflows. Talk with us today.

How to Add AI Pre-Screening to Your Workflow

Getting started doesn’t have to be complicated. Here are the steps:

1. Build or update a claims library
   • Collect all approved claims, risk statements, and references in one place.
   • AI uses this library to check content.
2. Choose pre-screening tools
   • Options range from vendor platforms to custom Salesforce/Veeva integrations.
   • Pick tools that integrate directly with your content hub.
3. Set rules and guardrails
   • Define exactly what counts as a violation (e.g., no unapproved disease claims).
   • Make sure AI logs every decision for audit trails.
4. Train teams to fix flagged issues
   • Writers and marketers need to know how to respond to AI suggestions.
   • This avoids slowdowns and builds trust in the system.
5. Measure results
   • Track metrics like cycle time, number of flagged issues, and error reduction.
   • Share improvements with leadership to prove ROI.

Risks and Watch-Outs

AI pre-screening is powerful, but it must be used carefully.

• Hallucinations: AI can sometimes generate incorrect warnings. Human review is still needed.
• Incomplete data: If the claims library is outdated, the AI will miss key risks.
• Over-reliance: Teams must not assume AI approval means regulatory approval. Final sign-off always comes from human reviewers.
• Regulatory uncertainty: Agencies like the FDA have not yet set formal rules for AI in review. Audit logs and human oversight are critical.

Related Blog

Want to see another example of AI reducing delays in pharma processes? Read our CI Health blog Adaptive Trials with Agentic AI: Real-Time Protocol and Operations. It explores how AI is helping clinical teams adjust trials in real time, speeding protocols without sacrificing compliance.

Why It Matters for Pharma

• Faster time to market: Shorter review cycles mean campaigns launch sooner.
• Lower compliance risk: Errors caught early prevent regulatory letters or fines.
• Smarter use of experts: Legal and medical reviewers focus on true risks, not small mistakes.
• Scalable global content: AI helps maintain compliance across affiliates and markets.

Conclusion

MLR review is a vital step for pharma marketing, but it doesn’t have to be the slowest one. AI pre-screening tools can act as a first line of defense, catching risky claims and missing references before content even reaches legal and medical reviewers.

Pharma teams that adopt pre-screening can expect faster approvals, lower compliance risk, and more consistent messaging. The key is pairing AI with strong claims libraries, governance, and human oversight.

CI Life works with pharma companies to set up AI-ready content hubs, compliance guardrails, and modular workflows. Schedule a consultation today to learn how we can help you cut review times and boost compliance confidence.