Photo by Louis Reed on Unsplash
The future of GenAI in pharma and healthcare isn’t about building bigger models — it’s about smarter retrieval.
Originally presented at the Fierce Pharma Webinar: “You Have the Model, Now What? Lessons on Making AI Work in Life Sciences and Breaking the Chatbot Mirage.” This is a five part quick read series that summarizes the panel discussion highlighting key topics of the conversations.
This recap of the Fierce Pharma x Vespa.ai panel distills how leaders from Novo Nordisk, Alkermes, and Harvard Medical School are reframing AI as a search and retrieval problem, powered by context, tensors, and explainability.
→ Full video: fierce-pharma-webinar
The take away: Why It’s a Search Problem
Search is the new foundation for intelligence.
Throughout the discussion, one idea kept resurfacing:
“You can’t trust a model to know everything. You must retrieve the right context — every time.”
At the end of the day, the healthcare & lifesciences value chain is all about search:
- Drug discovery → searches molecular space.
- Clinical teams → search patient cohorts across multi modal data
- Commercial teams → search for relevant brand content
- Payers search → member journeys.
Even AI itself searches its own embeddings.
That’s why the future of health & life sciences AI is not about building ever-larger LLMs, but creating smarter retrieval systems — where context is the currency. Vespa.ai is the engine powering it.
Design for retrieval first — and intelligence will follow.
