Pharma MLR teams are drowning in content reviews. With pharma companies producing 3x more materials than just five years ago, traditional review processes have become a bottleneck, preventing life-saving treatments from reaching more patients, more quickly. While AI promises relief, choosing the wrong type of AI can actually make the problem worse.

AI-powered solutions are everywhere, but not all solutions are the same. When looking at the landscape of AI, both in our everyday lives and in healthcare specifically, most tools and systems fit into two camps: general or specialized. 

When you have a specific, complex use case to solve, there’s only one route to go: specialized AI. Specialized systems, like the one that powers Revisto’s MLR review engine, adapt to the user’s unique needs and prevent vendor lock-in. 

"The difference between general and specialized AI in MLR isn't just about features—it's about understanding,” said Ferry Tamtoro, CEO & Co-Founder of Revisto. “General AI sees text to review; specialized AI sees regulatory risk, competitive positioning, and patient safety implications. When you're dealing with materials that can impact patient lives and cost millions in delays, that depth of understanding is crucial."

When we zoom into the healthcare and MLR space, you commonly find “jack of all trades, master of none” solutions. Too many tools are trying to do too many things; help too many types of users; solve too many problems. Read on for an exploration of the key benefits you can unlock when you choose a specialized, flexible system for your MLR needs: 

1. Better accuracy, deeper understanding

The most critical difference between general and specialized AI lies in accuracy and depth of understanding. General AI systems are trained on broad datasets from across the internet, giving them wide knowledge but shallow expertise. In contrast, specialized AI systems are trained exclusively on domain-specific, curated data that has been validated for accuracy.

For MLR review, this difference is literally the difference between compliance and catastrophe. For a promotional claim like "Drug X significantly reduces symptoms in 78% of patients." General AI might flag this as "potentially requiring substantiation" without understanding what actually needs to be done. It doesn't know that "significantly" requires statistical significance from adequate and well-controlled studies, or that the 78% figure must come from the primary endpoint of a pivotal trial, not a secondary analysis.

2. Quality over quantity

General AI systems are often marketed based on the massive scale of their training data, encompassing billions of web pages, articles, blog posts, and documents. But when it comes to MLR review, more is not better if the data quality is poor.

Internet medical content includes everything from peer-reviewed research to patient forum discussions to marketing materials that were never approved by regulators. Training AI on this mixed bag means the system learns patterns from both compliant and non-compliant sources, creating unpredictable outputs.

Specialized MLR AI takes the curated approach, which means every pattern the AI learns comes from verified, compliant sources. This results in output that is trustworthy.

3. Leveling up legacy systems 

Specialized AI tools are engineered to complement and plug into existing systems, not replace them. For example, if your team relies on an existing document management system for storing regulatory filings or marketing materials, a specialized AI can connect via APIs to pull data, automate compliance checks, or flag inconsistencies—all without requiring a costly system replacement. This plug-and-play approach minimizes disruption, preserves your existing investments, and lets you adopt advanced features incrementally. As an example, Revisto’s AI platform integrates seamlessly with Veeva Vault PromoMats, allowing users to continue to use the system they are familiar with while benefiting from specialized MLR AI optimization.

4. No vendor lock-in

All-in-one platforms can back life sciences into a corner, as it can be expensive, time consuming, and disruptive to switch tools if the one you’re using falls short or fails to evolve. Specialized systems, by contrast, are modular—like Lego bricks—allowing you to pick and choose the best tools for each task. A global life sciences firm might use Revisto for promotional material compliance checks while maintaining Veeva Vault PromoMats for content management and Salesforce for CRM functionality, mixing best-in-class vendors to suit their needs. When a better content management system emerges, they can swap it in without dismantling their entire MLR workflow. 

Because specialized AI systems work with domain-driven data formats and APIs, data and insights migration becomes significantly easier, your historical review decisions, approved claim libraries, and organization preferences can transfer seamlessly to new platforms without losing years of institutional knowledge. It’s a safeguard against being stuck with a vendor whose priorities diverge from yours.

5. Rapid onboarding

Specialized systems support quick and easy onboarding of new team members or external partners. Because specialized systems are built with specific life sciences workflows and tasks in mind, they’re naturally intuitive. Task-specific designs reduce the learning curve, allowing new hires to jump in and start using the tool effectively with minimal training. Similarly, external collaborators can integrate their work without having to learn the ins and outs of a massive platform. 

6. Flexible updates 

In MLR review, regulatory compliance is a moving target, with guidelines and regulations evolving almost daily. These regulatory changes, like a new FDA guideline on social media, for example, demand quick adaptation to avoid reputational and financial harm. Specialized systems can roll out updates swiftly—often in the background—ensuring compliance without halting your work. All-in-one platforms, however, might require extensive updates for multiple areas of the platform, often with a pre-determined schedule of once or twice a year. This results in delay to your campaigns and potential risk of noncompliance. With specialized tools, your team stays productive and compliant, even as the regulatory landscape shifts. This agility minimizes disruptions and keeps your projects on track.

Gain a competitive edge with Revisto

Revisto is the specialized system purpose-built for MLR review. Leveraging AI trained exclusively on pharma MLR data and seamlessly embedding with your existing workflows, Revisto accelerates time to market for life sciences marketing materials, without sacrificing quality of compliance. 

Unlike general and legacy tools which may need to be adapted to meet the demands of the MLR review cycle, Revisto was built specifically to address the nuanced MLR landscape and be a worthy partner to your MLR teams. This specialization transforms material review cycles into processes that happen in days, not weeks or months.

The choice between general and specialized AI isn't just about features—it's about outcomes. In an industry where a single day's delay can cost millions and where compliance is non-negotiable, that specialization is a must.

Contact us today for a free demo and see how specialized AI can transform your MLR review process.