75% of knowledge workers used AI in the workplace in 2024, according to a recent report by Microsoft, and the biopharma sector is no exception. And while employees across marketing, compliance, legal, and medical are adopting AI tools at breakneck speed, not everyone knows exactly which type of AI-related technology they’re putting to work on their biggest challenges, and how can they help you? Let’s break it down. 

Natural Language Processing

Often abbreviated as NLP, natural language processing is a branch of AI that leverages algorithms to process, analyze, and interpret large amounts of natural language data, such as written text or recorded speech. It can make use of techniques like tokenization, sentiment analysis, and parsing to turn unstructured language into insights. 

Think of NLP like an expert editor who can not only synthesize vast amounts of content quickly, but also catch important nuances consistently and help software understand the meaning and details in text. It can even detect tone and sentiment in marketing copy to align with brand guidelines. 

NLP in MLR review 

In terms of MLR review capabilities, NLP can scan promotional materials for unapproved claims of off-tone messaging, saving marketing teams precious time. It can also be trained to recognize language patterns that conflict with regulatory standards or violate FDA or EMA guidelines. For example, if a promotional piece includes an off-label use statement, NLP can highlight it for review, reducing the risk of regulatory penalties. 

Overall, NLP serves as a “second set of eyes” for both marketers and legal and regulatory teams, saving hours of manual review, helping reviewers focus on the biggest issues detected, and providing peace of mind.  

Machine Learning

Machine learning, or ML, is a subset of AI where systems are trained on large datasets to identify patterns, make predictions, or automate decisions, versus being specifically programmed for a particular task. Machine learning depends on statistical models and continually improves as it processes more data over time. 

Machine learning is like an independent trainee who learns from experience. It studies examples, like past campaigns or historical comments, to get better at flagging issues—all without human intervention. 

Machine learning in MLR review

Machine learning models can be trained on a company’s historical MLR data, including approved claims, rejected claims, and company-specific compliance policies, then automatically verify that new promotional materials align with these standards. For example, if a company requires all claims to cite specific clinical trial data, ML can flag any claim lacking a reference, such as claiming a drug “improves outcomes” without providing evidence. This reduces the manual effort required to validate claims, speeding up approvals and ensuring consistency.

ML can improve over time by learning from feedback. For example, if a medical affairs reviewer frequently corrects an unsubstantiated claim, ML can automatically flag similar language for review in future drafts, minimizing repetitive corrections. 

It also has some powerful predictive capabilities. ML can analyze patterns in historical MLR rejections to predict potential issues in new materials. These capabilities allow teams to address issues before they escalate, reducing the risk of costly revisions or regulatory challenges. It can also predict potential issues based on historical data and 

Computer Vision

Finally, computer vision is an AI discipline that enables machines to analyze and interpret visual data, like images or videos, using deep learning models to detect objects and backgrounds, recognize actions and patterns, or identify anomalies.

Consider computer vision like a very detailed designer and inspector that checks details in visuals and ensures that every image and every frame is pixel-perfect and compliant. 

Computer vision in MLR review

In MLR review, computer vision can analyze visual elements in promotional materials, such as logos, fonts, and color schemes, to ensure they comply with brand guidelines. For example, if a brand requires a specific logo size or color hex code, computer vision can instantly detect deviations, like an outdated logo, an unapproved image, or incorrect shade of blue in an ad. This automation eliminates the need for marketing teams to manually inspect visuals, ensuring consistency across campaigns and freeing them to focus on creative development.

Computer vision is especially helpful in multi-channel promotional campaigns. It can ensure consistency across campaign visuals by analyzing multiple visual assets—such as banners, social media graphics, or video ads—to confirm they maintain consistent branding elements, like font styles or background patterns. This streamlines the workflow for marketing, who often juggle multiple assets, ensuring a cohesive campaign look without manual checks.

Revisto puts the best AI technologies to work for MLR reviewers

When you use Revisto to facilitate your MLR reviews, we put the latest AI technology to work streamlining the review process for every reviewer, from marketing to legal to medical. With direct integration with Veeva Vault PromoMats, Revisto seamlessly integrates with your existing MLR workflows, providing specific and actionable recommendations that help you maintain high quality standards while reducing time to market.  Contact us today for a demo and see it for yourself.