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PODCAST

From Models to Medicine

KAMI Think Tank

At KAMI Think Tank, we love cutting through the AI hype to showcase where the technology actually stands and how to use it meaningfully within the life sciences. Now, we"re bringing that same clarity with our community to a brand new podcast: From Models to Medicine. Every week, we sit down with a real practitioner in the life sciences, whether they"re working in the lab, leading innovation at a biotech, or building tools to better serve scientists. We discuss how they cut through the hype of AI

science life sciences

All Episodes

00:36:52

Episode 25: The 200-Page Protocol Problem AI Is...

From Models to Medicine ·

2026/09/30

In this episode of From Models to Medicine, we sit down with Angie Schwab, CEO and founder of Trialynx, who has spent 20 years watching clinical trials run over budget, over timeline, and over complexity for one reason: nobody got rid of the copy-paste. Angie makes a sharp distinction that most people in the AI conversation are blurring. That automation and AI are not the same thing, and deploying generative AI on top of unstructured workflows without fixing the foundation first is how you get

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00:27:59

Episode 24: Beyond Correlation - Teaching AI the...

From Models to Medicine ·

2026/09/16

This week, we sit down with Jean-Baptiste Morlot of Deep Life, who makes the case that the drug discovery field has been asking the wrong thing of its AI models. Correlation-based machine learning can find patterns, but it can't tell you why a cell is sick or what it would take to make it healthy again. Jean-Baptiste walks us through how single-cell sequencing unlocked the data volume needed to train something far more ambitious: a causal world model of cellular biology that learns the

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00:30:32

Episode 23: When Biologists Start Thinking Like...

From Models to Medicine ·

2026/09/02

This week, we sit down with Katie Lennon, CSO of Exthymic, who makes a case most bench scientists aren't ready to hear: biology isn't just a discovery discipline anymore. It's engineering. Katie walks us through what it actually looks like to build biology, hardware, and software concurrently, why that requires a shared language across disciplines, and what gets lost when teams hand off instead of collaborate.We also get into the unglamorous side of AI in the lab. More data

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00:45:04

Episode 22: The Model Was Never Built for Her

From Models to Medicine ·

2026/08/26

In this episode of From Models to Medicine, we sit down with Mark Amouzgar, co-founder and CEO of March Health. Mark opens with a striking stat: general-purpose AI models are only about 60% accurate in women's health. Sounding confident isn't the same as being clinically safe, and in this episode he breaks down exactly why generic AI was never built for this.We get into how March Health built a gender-aware digital twin, what it takes to wrap a model in real clinical logic, and why the

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00:34:57

Episode 21: Glass Box Wins - What Pharma Actually...

From Models to Medicine ·

2026/08/19

In this episode of From Models to Medicine, we sit down with Lavanya Anandan, Head of External Innovation, West Coast at CSL, whose path from bench scientist to Merck Group platform builder to UCSF Innovation Venture startup advisor gives her a rare view of where AI in drug discovery is delivering versus where it's mostly promise. She makes a sharp case that the companies winning in AI-bio aren't necessarily the ones with the best models, and the reason why might surprise you.We get into

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00:41:17

Episode 20: The Build vs. Buy Question Every Pharma...

From Models to Medicine ·

2026/08/12

We chat with Karl Moritz, CEO of Reliant AI, now part of Cohere, whose career took him from NLP research to McKinsey to building AI infrastructure for drug development. Karl reminds us that clinical trial success rates have been stuck years, and the golden age of AI-powered target discovery isn't fixing it.Karl lays out a sharp framework; own the science, rent the AI plumbing, and explains why building your own data ingestion pipeline is as misguided as writing your own operating system

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00:33:03

Episode 19: Flow Cytometry, Biomarkers, and the AI...

From Models to Medicine ·

2026/08/05

For this episode, we sit down with Banishree Saha, Global Biomarkers Lead at Takeda, who has worked in fields such as academic liver biology to translational biomarker work, over the course of her 20-year career. She walks us through what biomarkers really are, how flow cytometry went from single-color panels to capturing 40 proteins simultaneously, and why that explosion in data has made systems thinking the only way forward.Banishree talks about AI's strengths and weaknesses, and makes the

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00:40:08

Episode 18: Why Does mRNA Keep Breaking AI Models?

From Models to Medicine ·

2026/07/29

In this episode, we sit down with Chris Nelson, an mRNA scientist at nCode Bio whose career has taken him from building blood transfusion matching tests now used by the NHS, to cancer genomics at Personalis, to the frontier of mRNA sequence design. Chris breaks down why the untranslated regions of mRNA might actually change how we dose therapeutics, reduce side effects, and engineer cells inside the body entirely.Then we talk about why AI has conquered protein folding but keeps hitting a wall with

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00:41:10

Episode 17: Garbage Data, Toxic Exposure, and the AI...

From Models to Medicine ·

2026/07/22

In this episode of From Models to Medicine, we sit down with Sarah Daniels, an environmental health researcher, who makes the argument that the chemicals making us sick might also be our best clues for building better drugs. She traces that idea from a 1976 discovery about dioxins all the way to an FDA-approved psoriasis treatment in 2023 — and asks what AI could accelerate if the field finally got its data act together.Sarah brings up where AI in environmental health is genuinely stuck; siloed

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00:37:35

Episode 16: Why Chemistry has Held Drug Discovery Back

From Models to Medicine ·

2026/07/15

Stan Jastrzębski is the co-founder of molecule.one and a deep learning researcher who made a deliberate pivot into synthetic chemistry. In this episode, he explains why chemistry is the real bottleneck in drug discovery today. Unlike biology, which has the Protein Data Bank and tools like AlphaFold, chemistry lacks the massive, balanced datasets AI needs to work. Scientific literature makes it worse, publishing wins and burying failures, which starves models of exactly the negative data they learn

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00:44:50

Episode 15: Stacking AI with Quantum in the Sciences

From Models to Medicine ·

2026/07/08

Anna Benefiel has spent over 20 years in healthcare innovation, and she's tired of watching life sciences sit on the sidelines of quantum computing. In this episode, she makes a direct case for why pharma needs to stop treating quantum as a future problem and start treating it as a present opportunity. We get into what quantum actually does that classical computing cannot, why probabilistic nuance matters for complex biological problems, and where the real near-term value lives: clinical trial

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00:45:47

Episode 14: The Drug Discovery Problems AI Alone...

From Models to Medicine ·

2026/06/24

In this episode of From Models to Medicine, we speak with Vid Stojevic, the co-founder and CEO of Kuano, a Cambridge-based company using quantum algorithms and AI to tackle the drug discovery problems that traditional computational chemistry keeps failing. In this episode, he explains why he deliberately ignored the broad platform play and went narrow instead, targeting the specific early-stage problems where getting the physics right changes everything. We get into what a "quantum lens"

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00:30:01

Episode 13: The Microbiome is Messier Than You Think

From Models to Medicine ·

2026/06/17

Jenny Yang is the co-founder and CEO of Outpost Bio, where her team is working to make human microbiology computable. In this episode, she breaks down why bias in ML models is so easy to miss. High overall accuracy can hide terrible performance on specific subgroups, and in healthcare, that gap has consequences. She traces the problem upstream, from skewed training datasets to the way clinical definitions themselves carry historical bias, and explains the real trade-offs involved in trying to

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00:49:14

Episode 12: Your Life Sciences Data Isn't Ready for AI

From Models to Medicine ·

2026/06/10

Bogdan Knezevic is the CEO and co-founder of Kaleidoscope Bio, and he's seen enough failed AI implementations to know where they almost always break down. In this episode, he walks us through what minimum viable data standardization actually looks like in practice, why consistent naming conventions and structured data entry matter more than people want to admit, and what every biotech CEO should ask their team before writing another AI budget line.We also get into a guardrails conversation and

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00:48:34

Episode 11: From the Gut to the Brain - Rethinking...

From Models to Medicine ·

2026/06/03

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

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00:38:10

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

From Models to Medicine ·

2026/05/27

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

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00:43:43

Episode 9: Mitochondria, Machine Learning, and a Few...

From Models to Medicine ·

2026/05/20

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

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00:42:20

Episode 8: The Equity Gap in Diagnostic AI

From Models to Medicine ·

2026/05/13

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

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00:45:04

AI at the Bench: The New Wet Lab Workflow

From Models to Medicine ·

2026/05/06

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

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00:45:38

Digital Twins, Real-World Evidence, and the Future of...

From Models to Medicine ·

2026/04/29

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

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26 results

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