Top 7 GenAI Use Cases for Pharma Companies in 2025

The 7 best ways for pharmaceutical companies to use GenAI in 2025

Pharma leaders are no longer asking if they should use Generative AI (GenAI); instead, they are asking where and how. GenAI is quickly becoming a strategic tool across the value chain, helping to speed up research and development cycles and lighten the load on pharma teams.

But not all cases are the same. Some are grown up and ready to be used. Others still pose a risk or need careful interpretation of the rules. In this blog, we talk about the seven GenAI use cases that pharmaceutical companies should focus on in 2025.

1. Making medical content

GenAI is changing the way pharmaceutical companies make clinical and scientific content in a big way. Language models can do the following with the right prompts and limits:

• Write answers to common questions from HCPs
• Make templates for HCP communication that are unique to each person.
• Summarise medical research for teams within the company
• Create patient education materials in more than one language

Pros: Faster time to publish, less human work, and more personalisation.

Things to think about for compliance:
Every output must go through a medical/legal/regulatory (MLR) review. Integrations with content repositories make sure that only approved information is used.

2. Sort and automate requests for medical information

Every year, large pharmaceutical teams get tens of thousands of questions. GenAI can: Sort the type of question (for example, dosage, safety, or adverse event)

Use approved content to suggest draft answers and send escalations to medical reviewers.

Pros: First-response time can be cut by up to 70%, and healthcare professionals can get things done faster.

Things to Watch: 

It is important to set accuracy limits and automatically flag safety signals for pharmacovigilance teams.

3. Faster review of literature and competitive intelligence

Medical Affairs, Regulatory, and R&D teams depend a lot on keeping an eye on scientific publications and what their competitors are doing.

GenAI is able to:

• Keep an eye on and summarise thousands of new articles
• Focus on drug interactions, study results, or KOL insights
• Monitor any alterations to the trials or labels of your competitors.

Advantages: Insights that are almost real-time with less manual work.

Best Practice: Use curated data lakes and human curation checkpoints with GenAI.

4. Information on how to design and carry out clinical trials

Teams can use GenAI models that have been trained on historical trial data, EHRs, and patient registries to:

• Suggesting trial designs
• Making guesses about changes to the protocol
• Offering site locations based on overlapping eligibility
• Predicting when hiring will happen

Benefits: fewer changes to the protocol, faster recruitment, and better site performance.

It is important to establish strong partnerships with data providers and to implement safe methods for sharing data.

5. Programs to help each patient in a unique way

Support programs are an important part of managing the lifecycle after launch. GenAI makes it possible to:

• Chatbots that can answer questions about coverage, dosing, or onboarding
• Help that is specific to your language
• Finding and raising the emotional tone
• Automated follow-ups that depend on how the patient acts

Pros: Better patient experience and less work for support teams.

Be careful: GenAI that interacts with patients must follow HIPAA, GDPR, and local privacy laws.

6. Help with writing and submitting regulatory documents

GenAI can help you write and improve structured documents like:

• Brochures for investigators
• Summary of the features of the product (SmPC)
• Reports on clinical studies (CSRs)
• Answers to questions from health authorities

Pros: Consistency across documents, faster iteration cycles, and better writing.

Tip for compliance: All outputs must be able to be checked, have a clear version history, and be able to be traced back to the source data.

7. Case Narratives for Pharmacovigilance

Writing case narratives by hand takes a lot of time. 

• GenAI can get case information from structured data
• Make first drafts of safety stories
• Suggest checks for consistency across similar cases

Pros: Less time spent on each case and more consistency.

Guardrails: Before submission, a qualified safety doctor must still review the document.

Things to Think About When Implementing GenAI for Pharma Teams

1. Control and Oversight

Create AI governance boards to approve use cases and keep an eye on risks.

Set rules for what is okay to use, how to check things, and how to review them.

2. Prompt Engineering and Fine-Tuning

Make prompts that don’t cause hallucinations and encourage truthful answers.

For content that is based on sources, use retrieval-augmented generation (RAG).

3. Stack of technology and architecture

You can use enterprise-safe LLMs on platforms like Salesforce, Microsoft Azure, or AWS.

Add GenAI to your current CRM and medical content systems.

4. Training and managing change

Give the Med Affairs and Regulatory teams more skills so they can work with AI.

Make it clear what GenAI can’t do to stop people from relying on it too much.

5. Alignment with ethics and fairness

Check for bias on a regular basis, especially when dealing with patients.

Make sure that people who don’t get enough services are equally represented in the training data.

2025 Is the Year to Get Things Done GenAI Pharma teams have tried out GenAI pilots. It’s time to go from pilot to platform now.

You can start using the most mature use cases today, like med info triage, content generation, and patient support, and see a measurable return on investment. Some things, like designing clinical trials and writing PV narratives, need more oversight but have a lot of potential.

GenAI can change the cost and quality of drug development if it has the right partners, infrastructure, and compliance lens.

Newpage helps life sciences companies go from being interested in GenAI to being able to use it safely, ethically, and on a large scale.