AI for Medical Affairs: From Medical Leadership to MSLs

A recent Alucio and MSL Society survey found that 95% of Medical Affairs executives recognize AI's impact on their teams, and 97% of MSLs believe it can improve their effectiveness with HCPs. Even with that level of interest, many organizations are still waiting on one large, centralized AI system instead of asking what already works today.

Scott Thompson (Co-CEO, Acceleration Point) and Eric Chen (CTO, Alucio) walk through two AI applications already in use in the field: content preparation for HCP meetings and insight generation from CRM data. Chen's demonstrations rely on Retrieval Augmented Generation (RAG), an approach where the AI searches an organization's own approved content first and grounds its answer in that material rather than generating from scratch, reducing the risk of fabricated information. Rather than a generic AI overview, Thompson and Chen address the compliance, integration, and specificity barriers that have slowed most Medical Affairs AI rollouts, using live, in-platform examples. If your team is still waiting for the big AI project, this is the session that shows what's already working.

What You'll Learn

• Four recurring barriers slow most Medical Affairs AI rollouts: waiting on one large, IT-led AI project, tools that sit outside day-to-day workflows, generic AI that doesn't understand Medical Affairs context, and AI policies still being finalized.
• In Alucio's Beacon platform, AI can assemble a tailored HCP presentation from an organization's previously approved slides and respects slide-dependency rules that keep fair-balance requirements intact.
• Newer AI techniques need few or no training examples ("zero-shot" or "few-shot" learning, as Eric Chen describes it) to identify and assemble the right approved content for a specific meeting.
• In Kwello, AI can summarize thousands of free-text insight submissions, such as 12,000 records in one demonstration, while linking every summary point back to the specific insight it came from.
• AI can flag a compliance concern at the moment an MSL is entering an insight; in one live demo, it stopped a submission written as an inappropriate quid pro quo statement before it was ever submitted.
• A separate global survey of 367 Medical Affairs professionals across 48 countries, published in the peer-reviewed journal Cureus, found broad interest in AI adoption and its perceived effect on MSL roles, reinforcing that this is an industry-wide shift rather than an isolated trend.

About Our Speakers

Scott Thompson is Co-CEO of Acceleration Point, where he has spent the last 15 years helping Medical Excellence leaders build effective Medical Affairs organizations. He hosts Acceleration Point's ongoing webinar series on practical AI applications for Medical Affairs and field teams.

Connect with Scott on LinkedIn: https://www.linkedin.com/in/medical-excellence/

Eric Chen is Chief Technology Officer at Alucio, where he leads development of Beacon, the company's scientific exchange platform for Medical Affairs teams. He also contributed to a recent Alucio survey conducted with the MSL Society on AI adoption among Medical Affairs executives and field MSLs.

Connect with Eric on LinkedIn: https://www.linkedin.com/in/ewchen1/

FAQ

What is a RAG (Retrieval Augmented Generation) implementation in Medical Affairs AI tools?
Retrieval Augmented Generation (RAG) is an AI technique where a search of an organization's own approved content runs first, and that content becomes the basis for the AI's answer. Eric Chen, CTO of Alucio, describes this as a way to manage the risk of fabricated information, since the AI is grounded in verified source material rather than generating an answer from scratch.

How does AI keep an MSL's presentation content fair and balanced?
According to Eric Chen, platforms like Beacon can define slide-dependency rules so approved content moves together as a set. An AI-assembled presentation follows those same rules, keeping it consistent with a manually built, compliance-reviewed deck.

How much training data does an AI model need to select relevant Medical Affairs content?
Very little. Eric Chen explains that newer AI techniques use "zero-shot" or "few-shot" learning, meaning the system can identify and assemble relevant approved slides with few or no prior training examples.

What are the most common barriers to deploying AI in Medical Affairs?
Scott Thompson outlines four recurring barriers: waiting on one large, IT-led AI project; AI tools that sit outside day-to-day workflows; generic AI that doesn't understand Medical Affairs context; and AI governance and compliance policies still being finalized.

Can AI help improve the quality of Medical Affairs insight data at the point of collection?
Yes. Scott Thompson demonstrates how AI can flag a submitted insight in real time, such as catching language that reads as an inappropriate quid pro quo statement before submission, and can prompt the MSL with more specific follow-up questions to strengthen the insight.

Curious how this applies to your organization? Let's talk.

About Acceleration Point Webinars

Acceleration Point webinars bring Medical Affairs and Field Medical leaders together to share practical insights, emerging strategies, and real-world perspectives on the challenges shaping the industry. Each session is designed to support the professionals driving scientific exchange, field medical excellence, and Medical Affairs transformation.

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