Six AI use cases already transforming Medical Affairs

Medical Affairs is evolving fast. The volume of data, the speed of evidence generation, and the expectations for scientific exchange are growing exponentially, while team sizes and budgets often remain the same.

Artificial Intelligence (AI) offers a powerful way to bridge that gap. When used thoughtfully and governed responsibly, AI enhances rather than replaces the strategic and scientific expertise at the heart of Medical Affairs.

In The AI for Medical Affairs Course with James Turnbull, participants explore where AI is already delivering measurable value today, and in the future. Below are six real-world use cases discussed in the course. Each one illustrates what is possible, what to watch out for, and how Medical Affairs teams can start experimenting safely.
 

1. AI-Powered Congress Reports

   Traditional congress reporting often takes weeks, delaying critical insights. AI can now process hundreds of abstracts, posters, and presentations to create structured, insight-ready summaries within hours.

   By combining natural language processing with human medical review, teams can extract key themes such as shifts in treatment paradigms, competitor messaging, and emerging data gaps almost in real time.

   ⭐ Value created: Faster turnaround, consistent insight quality, and broader visibility across global teams.

   💡 Tip: Start small with one congress and one therapy area, then build reusable workflows for others.
    

2. Personalised Internal Learning Summaries

   Internal training materials are rarely one-size-fits-all. AI can adapt learning modules and post-training summaries to each role, simplifying mechanisms of action for new MSLs or emphasising data interpretation for senior scientists.

   By analysing engagement data, the system can also identify collective knowledge gaps that guide continuous content improvement.

   ⭐ Value created: Higher engagement and retention, and a measurable increase of scientific confidence in the field.

   💡 Tip: Keep the AI limited to approved medical content to maintain consistency and compliance.
    

3. AI-Supported Advisory Boards

   Capturing insights from advisory boards is vital but labour-intensive. AI tools can now assist at every stage by helping design balanced questions, transcribing, summarising discussions, and clustering feedback into actionable themes.

   This creates a more transparent and auditable process, ensuring every expert’s input is captured, not just the loudest voices.

   ⭐ Value created: Richer insights, faster synthesis, and a defensible audit trail for compliance.

   💡 Tip: Always maintain human moderation. AI supports facilitation, it does not replace it.
    

4. AI-Driven Content Repurposing

   From one approved scientific source, such as a core claims deck, AI can generate multiple compliant derivatives including MSL slide notes, plain-language summaries, or internal Q&A briefs.

   This approach accelerates content creation and ensures consistency across teams and channels.

   ⭐ Value created: Less duplication, faster cycle times, and stronger alignment between global and local Medical Affairs.

   💡 Tip: Anchor all AI outputs to a single approved source document with automated citation tracking.
    

5. Personalised Interactive Congress Experiences

   Imagine every delegate receiving a custom congress itinerary that highlights relevant sessions, sends alerts for key presentations, and allows real-time note capture that feeds directly into central insight reports.

   AI assistants can now deliver this type of experience, helping teams navigate busy congress schedules and capture structured insights without losing context.

   ⭐ Value created: Sharper focus at congresses and higher-quality post-event reporting.

   💡 Tip: Pilot this internally before considering any external-facing versions to ensure proper data governance.
    

6. AI-Powered Insights Analysis

   The next frontier for Medical Affairs lies in unified insights. AI can aggregate data from field interactions, medical information, publications, and congresses to identify patterns in sentiment, unmet needs, or emerging safety topics.

   This transforms scattered qualitative feedback into quantitative evidence that can guide strategy and evidence-generation planning.

   ⭐ Value created: Early signal detection, data-driven decision-making, and stronger cross-functional alignment.

   💡 Tip: Success depends on structured data input and a consistent taxonomy across functions.

These six use cases show that AI in Medical Affairs is not futuristic; it is already happening. 

The most successful teams start small, embed governance from the start, and scale only what demonstrably works. They focus on designing workflows that amplify the scientific judgment, communication, and compliance discipline that define the function.