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

Episode 11: From the Gut to the Brain - Rethinking Parkinson's with AI

In this episode of From Models to Medicine, we sit down with Minna Schmidt [https://www.linkedin.com/in/minna-schmidt-80390a97/], a postdoctoral researcher at the Buck Institute for Research on Aging. Minna walks us through Braak's hypothesis and the emerging "brain-first vs. body-first" framing of the disease, explaining how symptoms can appear up to 30 years before a clinical diagnosis is ever made. We also get into the data side of the work. Minna uses a dataset with over 54,000 participants and talks honestly about what AI actually does and doesn't unlock when you're staring down that volume of microbiome and clinical data. She uses LLMs to organize her thinking, speed up literature reviews, and learn basic programming, while being clear-eyed about where the field's biggest bottleneck actually is: not the tools, but the data itself. ---------------------------------------------------------------------- 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.

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.