Book a Demo


3 Ways AI Can Improve Pharmaceutical Labels

Pharmaceutical labels are often complex and riddled with medical jargon, making them difficult for patients to completely understand. AI is helping pharmaceutical companies mitigate this challenge so that patients gain clarity on their medications. In this post, we discuss the top 3 ways AI is revolutionizing pharmaceutical labels to enhance patient comprehension.

The readability of pharmaceutical labels is a growing problem, particularly in regions with aging populations where drug consumption is growing rapidly (to the tune of a 3-6% CAGR). In the United States, for example, drug labels have been criticized for being overly complex and difficult to read; each year, over a million Americans experience a health problem because they don’t take their medicine as their doctor intended.

Artificial Intelligence (AI) can help pharmaceutical companies improve the overall clarity and readability of drug labels. Through natural language processing and generative AI, pharmaceutical companies can transform label sentences and sections into language that is both easy to understand and makes sense for any given local label geography or language.

In this blog, we review 3 ways that AI is improving pharmaceutical labels and how you can implement this technology at your organization.

#1 – AI Enhances Overall Label Clarity

Each year, an estimated 90 million adults in the United States misunderstand prescription drug labels or have trouble following their directions. According to Dr. Joanne Schwartzberg, a member of USP’s Nomenclature, Safety and Labeling Expert Committee, “So many people make mistakes when taking their medicines. Many mistakes result from dosing directions. For example: patients may read ‘Take two pills twice daily,’ and believe it means to ‘take two pills a day,’ rather than the intended instruction to take a total of four pills.”

Studies also show that consumers find drug names (generic vs. brand name) to be confusing, and words like “indications,” “precautions,” and “contraindications” contained within many labels to be difficult to understand. This lack of clarity can cause patients to not take the medication at all, take it to treat a different illness or symptom than the one it was prescribed to treat, or take an incorrect dose, which can have unintended health consequences that range from minor irritations to antibody-dependent enhancements to death.

To make information easier to understand, language on a label should:

  • Be simple, clear, concise, and familiar
  • Use standardized terms to promote understanding through consistent language
  • Be provided in the patient’s preferred language whenever possible 

AI is making this possible, and can improve label readability through its revolutionary tools and capabilities. Some of these are: 

Natural Language Processing (NLP)

AI, particularly NLP algorithms, can be employed to analyze and understand the complexities of prescription drug labels. By breaking down the language used in dosing instructions, warnings, and other relevant information, NLP can help simplify and rephrase the content to enhance clarity.

Personalized Communication

AI can tailor information based on individual patient profiles, taking into account factors such as language preferences, health literacy levels, and medical history. This personalization can help ensure that patients receive instructions in a manner that is most easily understood by them.

Behavioral Analytics

AI can analyze patient behavior patterns to identify potential issues with medication adherence. By monitoring trends and deviations from prescribed regimens, AI can flag concerns and prompt timely interventions, such as additional clarification or support.

Continuous Learning and Improvement

AI systems can continually learn from user interactions, feedback, and new medical information. This enables ongoing refinement of communication strategies, ensuring that the system evolves to meet the changing needs of patients and healthcare providers.

#2 – AI Can Develop Standardized Terms

Simple, standardized language plays a key role in overall patient comprehension. When pharmaceutical labels use terms that are familiar to patients, those patients will gain understanding and clarity. 

Organizations can feed AI with specific language to inform it of the reading comprehension level (i.e., 8th grade level) at which it should be generating label content. Then, to identify appropriate labeling terms and phrases at the specified comprehension level, AI crawls existing pharmaceutical data repositories and RIM systems using natural language processing to classify data as either structured or unstructured, aggregate repositories and file shares, and extract text to gain insight into data and context.

By overlaying a pharmaceutical company’s existing labeling ecosystem, AI can harmonize metadata, properties, and attributes and store them as controlled, compliant components. These controlled components can then be used to create a library of words and phrases that are automatically applied in the label creation and translation processes. 

Then, approved labeling documents are indexed based on keywords and phrases. Keywords and phrases are in turn indexed based on their usage count in documents, and labeling teams can then select and mark certain phrases (i.e., those that are simple and easy to understand) as recommended phrases to be applied to future labels with higher priority.

#3 – AI Has Translation Capabilities

It’s critical that pharmaceutical labels reflect the local regulatory requirements and language in the geographies in which the drug is sold. If the label is in the wrong language, uses incorrect translations, fails to include key regulatory language, or leverages unfamiliar words and phrases, communication between patients and physicians, adherence levels, clinical outcomes, and patient safety could be negatively impacted. 

At most pharmaceutical companies today, the process of generating labels across multiple geographies into the correct format and language is very manual. For example, since there is only one core data sheet per drug, a Certified Clinical Documentation Specialist (CCDS) often does not include the proper regulatory language that is required in one or more geographies. Even if a CCDS did include regulatory language, it is likely that it would only work for a select geography. Thus, pharmaceutical companies spend countless hours and millions of dollars each year on manually transforming these documents into local label formats. 

A sophisticated AI authoring tool can identify CCDS sections and automatically transform them into the approved regulatory language for any given geography. To avoid issues associated with literally translating CCDS from English to other languages,  AI can utilize a translation engine that searches repositories in multiple languages, with a specific eye toward cultural context, and returns results in the new language. 


Digital transformation enabled by AI is affecting virtually every aspect of the pharmaceutical value chain and is bringing us closer to a future that is defined by radically interoperable data and consumer-driven healthcare. 

Glemser’s ComplianceAuthor® AI has the power to help pharmaceutical companies create health authority-approved, patient-specific labels. This structured content management tool developed specifically for pharma can streamline the way you author and manage regulatory content. And by leveraging AI behind the firewall in a secure manner, ComplianceAuthor® AI can advance life sciences organizations by using automation to improve time to market while reducing the time it takes to author, edit, and submit a label for approval.

Want to use AI to improve the readability of your pharmaceutical labels? Contact Glemser today to get started. 


[1] Global Medicine Spending and Usage Trends: Outlook to 2025 – IQVIA

[2] Do You Find Prescription Labels Hard to Read? You Are Not Alone | Quality Matters | U.S. Pharmacopeia Blog (

[3] Do You Find Prescription Labels Hard to Read? You Are Not Alone | Quality Matters | U.S. Pharmacopeia Blog (

[4] USP is an independent, scientific nonprofit organization focused on building trust in the supply of safe, quality medicines.

- Workflow Step User Automated
Crawl labeling ecosystem to understand content and context of data
Process unstructured data and convert to structured data
Search NLP database for keywords, phrases, images, videos, etc.
Recrawl data and recognize changes to data at the source and recommend next best action
Transform and translate unstructured data into language narrative for local label geography
Review language narratives for accuracy and select narrative