Category: MedTech

Navigating MLR with AI

What is MLR and why does it matter in pharma and MedTech marketing?

MLR stands for Medical, Legal and Regulatory review. It is a mandatory, cross-functional process that ensures that all promotional, educational and informational materials are:

accurate and balanced
scientifically substantiated
compliant with global and local regulations

In highly regulated industries like pharmaceuticals and MedTech, MLR approval is a critical checkpoint before any HCP-facing or public content goes live. Failure to follow MLR requirements can result in:

Regulatory penalties
Product delistings
Legal liabilities
Damaged reputation

As content volume increases with the rise of omnichannel and AI-generated materials traditional MLR workflows can become a bottleneck unless modernized.

How can AI improve MLR workflows?

AI supports MLR processes in three major ways:

1. Content Pre-Screening

AI tools can scan drafts to:

Flag non-compliant language
Identify missing references or claims
Highlight risky phrasing (e.g., off-label implications)

This allows compliance teams to focus human review where it’s most needed, accelerating the overall review cycle.

2. Co-Pilots for MLR Teams

AI-powered assistants can:

Suggest alternative compliant language
Autofill references from approved claim libraries
Surface similar approved content as templates

These tools significantly reduce manual effort, especially during content revisions.

3. Approval of Modular Content

The modular content model breaks content into pre-approved blocks. AI can then:

Assemble high-volume personalized content on demand
Guarantee compliance by combining only validated modules
Eliminate hallucination risk (a common issue with generative AI)

This method not only accelerates content production but also reduces MLR review time by up to 60%.

How does AI integrate into the MLR approval chain?

Phase AI Role Benefit

Phase	AI Role	Benefit
Pre-MLR	Drafting, tone adjustment, auto-translation	Faster initial content creation
MLR Review	Pre-screening, red-flagging issues, checking against literature references	Prioritizes human review efficiently
Post-MLR Deployment	Matching with HCP profiles, tagging usage	Tracks content reuse & optimization

The AI’s involvement must be transparent, traceable, and auditable, ensuring compliance with internal SOPs and external guidelines.

What are the risks and limitations of AI in MLR?

While AI accelerates MLR processes, there are important caveats:

Hallucinations: Generative AI may invent plausible-sounding but false claims. Limiting AI to assembly of approved blocks solves this.
Regulatory uncertainty: Global health authorities (e.g., FDA, EMA) have not yet fully defined how AI-assisted content creation fits within promotional regulations.
Tool validation: Any AI tool used must undergo proper validation, risk assessment, and documentation — especially in GxP environments.

Best practices for AI-driven MLR transformation

Separate AI use by stage
- Use generative AI only before MLR to assist with content ideation or rephrasing.
- Use only pre-approved content for AI assembly after MLR.
Invest in modular content strategy
- Pre-approve reusable content blocks to scale faster without repeated review cycles.
Enable “compliance by design”
- Integrate claim libraries, brand guardrails, and reference checkers directly into content tools.
Improve the supply chain, not just speed
- Faster content creation is meaningless without scalable MLR capacity.
Partner with experienced vendors
- Choose platforms and agencies that specialize in life sciences compliance and can demonstrate audit readiness.

Summary

Modern MLR is no longer just a gatekeeper. MLR process is a strategic enabler of fast, scalable, and compliant pharma marketing. When powered responsibly by AI, it can cut approval cycles from weeks to days, support omnichannel campaigns, and free up expert reviewers for higher-value tasks.

The future of compliant content in life sciences is modular, AI-assisted, and human-supervised — and it’s already underway.

This text has been enganced with GenAI tools.

Tags AI, MedTech Commercialization, omnichannel, pharma marketing

MedTech Pharma Marketing

Top Use Cases of AI in Pharma & MedTech

Post author By Piotr Wrzosinski
Post date 06/08/2025
No Comments on Top Use Cases of AI in Pharma & MedTech

What are the main forms of generative AI being leveraged in pharma marketing?

Generative AI can be categorized into three general uses:

Content Generators: These produce texts, images, and video rapidly, and can also refine content by changing tone, simplifying information, or fine-tuning messages for specific audience segments.
Answer Engines: These are new forms of ‘search’ that generate direct answers (text, images, video) rather than just providing links. They can be used to extract insights from market research reports or summarize clinical studies.
Agents (Large Action Models – LAMs): These forms of AI act to achieve specific goals without specific instructions or human oversight. For example, an AI agent could review keynote Q&As and social media reactions to assess the medical community’s response to a drug launch. These are considered the most controversial due to their autonomous nature.

What are the primary use cases for AI in pharma and MedTech marketing?

There are four broad categories of AI use cases in pharma and MedTech marketing:

Strategic Insights Generation: AI can analyze vast amounts of both structured (e.g., CRM data) and unstructured data (e.g., government policy documents, articles on HCP preferences) to generate actionable insights and trends. This can improve understanding by up to 30%. However, caution is advised as AI can produce plausible but incorrect results (“AI hallucination”).
Marketing Content Generation: AI is crucial for omnichannel strategies that require high content volume for personalization. It can rapidly generate various content formats (texts, images, video, audio) and reduce content creation costs by 30-50%.
MLR Acceleration (Medical, Legal, Regulatory Review): AI can modernize labor-intensive MLR processes, which typically take 21 to 56 days for approval. AI tools can pre-screen materials, flagging problematic content for detailed review and fast-tracking compliant assets. AI co-pilots can also assist MLR staff with referencing claims and rephrasing options, potentially accelerating content approvals two to three times.
Field Force Enablement: AI benefits customer-facing staff like sales teams and Medical Science Liaisons (MSLs) by improving training, account planning, and content delivery. AI can provide immediate access to drug information, offer coaching for specific situations, and integrate with CRM to help reps prioritize accounts and personalize HCP engagements based on “next best” message or format recommendations.

Tags AI, medical devices, MedTech Commercialization, pharma marketing

MedTech Pharma Marketing

Multichannel vs Omnichannel in Pharma Marketing

Post author By Piotr Wrzosinski
Post date 06/08/2025
No Comments on Multichannel vs Omnichannel in Pharma Marketing

What is the difference between multichannel and omnichannel marketing in pharma and MedTech?

Multichannel marketing uses multiple independent channels (like television, social media, email, websites) to communicate with customers. In this approach, channels often operate as silos, repeating the same message, with the focus remaining on the product rather than the user. It lacks a connecting node between channels.

Omnichannel marketing also engages multiple channels, but they are all integrated and coordinated to provide a unified, seamless, and personalized experience for the customer, who is placed at the center of the marketing funnel. This approach ensures that information is available precisely when needed, encourages deeper content exploration, and reinforces messages across touchpoints, ultimately improving engagement and fostering trust.

According to McKinsey, correctly implemented omnichannel models can lead to 5-10% revenue growth and 10-20% marketing efficiencies and cost savings for pharma companies.