Episode 7: AI at the Bench: The New Wet Lab Workflow

Episode 10: When AI Gets It Wrong, Patients Pay the Price

In this episode of From Models to Medicine, we sit down with Sal Tejani [https://www.linkedin.com/in/sal-tejani-41418870/], Associate Director for Field Medical Affairs at Regeneron*, who started his career catching dangerous prescription errors at CVS and never lost the instinct for finding the lever that actually moves things. Today that instinct is pointed squarely at AI; how to use it, when to trust it, and when it will absolutely get you into trouble. Sal gives us an honest, practitioner-level view of what AI looks like inside a major pharma company: the tools that are actually useful, the guardrails that are non-negotiable, and the human judgment that no model has figured out how to replace yet. Plus, he closes with a personal story that reframes the whole conversation about why any of this actually matters. This episode was sponsored by CleanSpace [https://www.cleanspaceus.com/]. CleanSpace [https://www.cleanspaceus.com/] designs, manufactures, and installs advanced controlled environments—delivering complex projects months faster with guaranteed costs and uncompromising performance. Please contact Chelsea for more information or with any questions at CLauridsen@CleanSpaceus.com. *Thoughts brought up on this podcast do not represent the views of Regeneron.

Episode 9: Mitochondria, Machine Learning, and a Few Hard Lessons

Rachel Jacobson [https://www.linkedin.com/in/racheldevayjacobson/] has spent her career moving between some of the most demanding corners of life sciences before founding Powerhouse Biology. In this episode, she traces that journey and explains why, after all of it, she keeps coming back to mitochondria. We get into what it actually takes to bridge biology and machine learning inside a lab culture, why asking "stupid questions" across disciplines is a feature and not a bug, and what she had to unlearn from traditional drug development to work effectively alongside ML engineers. We also dig into data design. Rachel makes a sharp case that data passing standard biological QC is not the same as data that's ready for a machine learning model. Uneven plate layouts, cell debris, different scientists handling samples can all create batch effects that quietly break your model before it ever sees a hypothesis worth testing. She connects all of this to a bigger argument about why human biological variability needs to be built into preclinical pipelines from the start, and why ML might finally give scientists the tools to do that seriously.

Episode 8: The Equity Gap in Diagnostic AI

In this episode, we sit down with Dr. Freddy Nguyen [https://www.linkedin.com/in/freddytn/], CEO and co-founder of Nine Diagnostics, whose background spans medicine, pathology, optics, and nanotechnology. Freddy shares how Nine Diagnostics is building a multiomics platform that helps cancer patients find out within days whether their treatment is actually working. We dig into why AI's real power in medicine lies in its ability to connect siloed data across molecular readouts, imaging, and clinical context, and why treating patients as more than just their diagnosis is the only way to build tools that actually hold up in the real world. We also get into the harder conversation: where AI in clinical workflows breaks down. It's a candid, technically grounded conversation about what equitable AI in medicine actually requires.

Episode 7: AI at the Bench: The New Wet Lab Workflow

In this episode of From Models to Medicine, we sit down with Elisa Martin Perez [https://www.linkedin.com/in/elisa-martin-perez/], a postdoc at University of California, Berkeley, to talk about how non-coders are starting to use AI in their day-to-day work. From learning R through conversation to making sense of massive CRISPR screens, Elisa shares how AI is becoming a practical tool for navigating data, checking experimental design, and cutting down on the kinds of manual tasks that quietly consume hours in the lab. We also get into the hesitation many scientists feel around adopting AI, where the technology actually helps (and where it doesn’t), and why it still falls short of running experiments end-to-end. Along the way, we touch on lab logistics, data overload, and what it means to use AI as a thinking partner rather than a replacement.

Episode 6: Digital Twins, Real-World Evidence, and the Future of Clinical Trials

What does it actually take to make AI work inside a pharmaceutical company and why do so many efforts stall after the model is built? Pranay Mohanty [https://www.linkedin.com/in/pranay-mohanty-23133b10a/], from J&J Innovative Medicine, joins us to talk about how building the model is only one part of the work and what can happen when you try to apply that model to messy, real-world data. We dig into how teams are starting to use digital twins to simulate patients and rethink trial design, what it looks like to work alongside regulators like the U.S. Food and Drug Administration, and why keeping a human in the loop isn’t optional. Along the way, we share how AI is actually shaping portfolio decisions today and where the limits still are.