John Quackenbush, Professor of Computational Biology and Bioinformatics at Harvard T.H. Chan School of Public Health and Mark Kiel, Founder and CSO of Genomenon joined us for a candid fireside conversation on use of AI in biomedicine. Bringing optimism about AI’s potential into direct contact with skepticism about what it can truly prove, this discussion put two distinct perspectives into dialogue - pressure testing what AI can truly contribute to biomedical research and clinical decision-making.
In this conversation, John and Mark covered how evidence is gathered, processed, and structured - and how it’s applied to decisions in research, development, and clinical practice.
Key questions we covered include:
- Is generative AI advancing biomedical knowledge, or just industrializing confirmation bias?
- Are we deploying generative AI in the clinic before we’ve agreed on what counts as evidence?
- If an “insight” can’t be traced back to primary sources, is it useful or too risky for hypothesis generation?
- Have we mistaken fluency for understanding - do models generate genuine insight, or do we project insight onto them?
- Will generative AI reduce uncertainty in biomedicine or mainly repackage it faster?
This session will give you a clear, practical view of where generative AI actually helps across drug development cycles - what it can accelerate (and what it can’t), where the risks show up, and what defensible validation looks like before it informs decisions.
Who should watch:
This webinar was designed for pharma and biotech professionals applying AI across the drug development cycle, including teams in:
- Discovery & Translational Science
- Clinical Development & Trial Strategy
- Medical Affair & Real World Evidence
- Bioinformatics & Data Science
If you are making high-stakes decisions, and want a realistic understanding of what generative AI can and cannot support, this session is for you.
Watch the recording today!
