Can AI Make Sense of Pharma’s Messiest Data?

Human biology is extraordinarily complex, and researchers often have only fragments of information to work with. Brian Martin compares it to looking at a skyscraper through a keyhole: you can see something clearly, but only a tiny piece of the whole.

Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Brian Martin, CTO of Applied AI at Cloudera and co-founder of Rare Hopes NFP, to explore what one of the world’s most data-intensive industries can teach us about AI and decision-making.

Brian explains how pharmaceutical R&D turns sparse, fragmented data into insights that support drug discovery. With a new drug potentially requiring years of development and billions of dollars in investment, better predictions can have an enormous impact on how quickly promising treatments reach patients.

Paul and Brian explore:

• How knowledge graphs can reveal relationships hidden across fragmented data
• Where AI can connect qualitative patient experiences with quantitative research
• Why patient consent complicates how valuable clinical data can be reused
• How pharma teams can share knowledge without dismantling every data silo
• Why embedding technologists with scientists can accelerate AI adoption
• What other industries can learn from pharmaceutical data strategy

If you’re responsible for enterprise data or AI strategy, this episode will show you how lessons from pharmaceutical R&D can help turn fragmented information into knowledge that drives better decisions.

Stay in touch with Brian:

Brian Martin on LinkedIn: https://www.linkedin.com/in/brianm1028/

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