From Models to Medicine – Détails, épisodes et analyse

Join us in our first episode where we chat with Margo Georgiadis, CEO of Montai Therapeutics as we discuss what a model means in ML vs. life sciences and how she's been able to bridge the knowledge gap between these two practices for her team.

Welcome to From Model to Medicine, a podcast from KAMI Think Tank exploring how artificial intelligence is actually being applied across the life sciences industry.

Each episode, we speak with scientists, operators, and leaders working at the intersection of AI and biology, from drug discovery and clinical development to regulatory operations and beyond.

KAMI Think Tank is an educational organization that helps life science professionals learn how to use AI practically in their day-to-day work.

This podcast is about moving past the hype and understanding what it actually takes to bring AI from promising models to real impact in medicine.

We’re your hosts, Kamayani Gupta and Michelle Yi. This is From Model to Medicine.

In this episode, we sit down with Dr. Freddy Nguyen, 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.

In this episode of From Models to Medicine, we sit down with 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.

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, 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.

In this episode of From Models to Medicine, we sit down with Ashley Zehnder, the founder of Fauna Bio to explore one of the most creative bets in drug discovery: using extreme animal biology to unlock human therapeutics.

We dig into the graph neural networks powering Fauna Bio's drug discovery platform, the unglamorous data curation work that makes it all function, and why human-in-the-loop isn't a limitation but a design principle. Plus: what hibernation research could mean for long-duration human space travel.

What does emergency medicine have to do with large-scale knowledge systems? Jason Grafft joins us to talk about his non-linear path from EMS and simulation education to knowledge engineering and why the structured, fact-bound thinking that medicine demands is one of the most undervalued assets in the AI era. We dig into how he uses graph-based models to bridge clinical experts and technical systems, where LLMs are actually helping in entity resolution, and the practical advice he gives every clinician trying to break into tech.

In this episode of From Models to Medicine, we sit down with Rachel Thomas to discuss the growing gap between AI hype and scientific reality in the life sciences.

The conversation highlights the hidden costs of poor data quality and the necessity of domain expertise, featuring deep dives into real-world AI missteps, from a flawed enzyme classification paper to an app's mishandling of long-COVID data.

As pathology labs digitize, how do skilled practitioners learn to trust AI with patient diagnoses?

Michael Rivers discusses the critical importance of "explainable AI," keeping the human pathologist in the loop with the ability to override algorithms, and how these powerful new technologies will help address the global shortage of pathologists, ultimately bringing equitable, expert care to patients worldwide.

Rachel Jacobson 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.