From Models to Medicine

In this episode of From Models to Medicine, we sit down with Mila Morris, founder and CEO of Nova Arc, who came to women's health not from medicine but from architecture and systems design. Mila noticed something in her regenerative medicine clinic where women were arriving with stacks of fragmented test results from multiple specialists and no coherent picture connecting any of it. We get into why most ovarian research is built on mouse models that don't actually reflect human female biology, why there are only two publicly available pre-menopausal human ovary datasets not impacted by disease, and what it takes to build an AI platform grounded in real human tissue data using single-cell spatial transcriptomics. Mila also shares a 2024 Nature Aging study that reframed how the field thinks about ovarian aging entirely. It's a conversation about one of the most underfunded and under researched areas in medicine and what it looks like to fix it.Show Notes:www.novaarchealth.com

We sit down with Geri Stengel, Forbes contributor, founder of Ventureneer, and co-chair of the Women's Health Technology Coalition, who has spent years reporting at the intersection of AI and women's health. Geri opens with a number that frames everything: from 1977 to 1993, the FDA recommended excluding women of childbearing age from clinical trials. That's nearly half of the core historical dataset that AI models are trained on.We get into the specialized companies building clinical-grade tools on narrow, targeted women's health data, why women are actually consenting to share their data at rates of up to 97%, and a systemic problem most people haven't heard of: search engines and social media platforms are routinely flagging standard medical terms like menstruation and menopause as adult content. That suppression drives up acquisition costs, distorts fundraising, and ultimately widens the health disparity gap. It's one of the more eye-opening conversations we've had on the show.Show notes:https://ventureneer.com/https://www.forbes.com/sites/geristengel/https://geristengel.substack.com/

For this episode, we sit down with Kristeen Pareja-Navarro, co-CEO of Engram Bio, whose path from a farming family in the Philippines to peptide engineering for Alzheimer's disease at the Buck Institute is one of the more remarkable stories we've heard on the show. Kristeen walks us through her work trimming the KIBRA protein into a small functional peptide that rescues memory impairment in mice, and how tools like AlphaFold 3 and ESMFold have compressed years of structural biology work into days.But she's equally candid about where AI falls short. Intrinsically disordered proteins still break the models. AI summaries miss the nuances buried in figures and methods. And no amount of compute can answer the question the field is still wrestling with: how does memory persist when every cell in your body eventually turns over. A grounded, honest conversation about what AI is genuinely changing in early-stage drug development and what it isn't.Show Notes:https://www.biorxiv.org/content/10.64898/2026.06.03.729735v1.full.pdfhttps://www.nature.com/articles/s41586-025-09429-6

We sit down with Shawn Manchester, CEO of TripleBar, whose path from yeast DNA repair to metabolic engineering at MIT to biomanufacturing AI gave him a front-row seat to one of biotech's most underappreciated problems. Everyone funds discovery. Nobody funds manufacturing. And yet a molecule that can't be made reliably and cheaply at scale never reaches a patient. Shawn makes the case that biomanufacturing is where the real bottleneck lives, and why it's finally getting the AI attention it deserves. It's a refreshingly grounded conversation about what biological AI actually requires to work.

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 polished-looking documents that still require someone to fact-check every line.We get into how Trialynx compresses months of protocol documentation into 30 days, why a single source of truth changes everything downstream, and where Angie draws a hard line on what AI should never touch. The episode closes on a genuinely provocative idea: AI pseudo-clinical trials that run molecules through synthetic models of human biology before a single patient is enrolled.

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 underlying rules of disease rather than memorizing them.We get into how Deep Life's TwinCell platform works, why most virtual cell benchmarks are quietly inflated by popularity bias, and what it means to build an interpretable AI that a biologist can trust.

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 documentation, peer-checking metadata before an experiment is marked complete, and knowing exactly where LLMs quietly get things wrong. Katie also shares a surprisingly effective strategy for getting scientists over their fear of AI tools. Companies mentioned in the show: https://exthymic.com/https://oni.bio/https://www.potato.ai/https://ollie.ai/-------------------Sign up for KAMI Think Tank's Maven course here! Classes start in less than 10 days!!

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 average endometriosis patient sees eight different doctors before getting an accurate diagnosis. It's a candid conversation about a problem that doesn't get nearly enough attention.-------------------------Sign up for KAMI Think Tank's Maven course here!

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 what big pharma wants from an AI partner, why women's health data scarcity is quietly producing some of the most rigorous AI in biology, and what an unexpected finding in mRNA research tells us about where this whole field might be heading. Articles mentioned in show: https://endpoints.news/can-ai-do-scientific-research-billions-chase-hopes-of-superintelligence/Maven course: https://maven.com/kamithinktank/ai-literacy-for-life-sciences-healthcare

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. He's also candid about what general-purpose LLMs are actually good for, where purpose-built enterprise tools earn their keep, and why the audit trail question will separate serious AI vendors from everyone else. If you're a pharma or biotech leader trying to make sense of the build vs. buy debate right now, this one is worth your time.