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PODCAST

Practical AI in Healthcare

Steven Labkoff, MD and Leon Rozenblit, JD, PhD

AI promises to transform healthcare—but real, scalable impact remains rare. Practical AI in Healthcare cuts through the noise to showcase real-world use cases delivering business value today. Hosted by senior leaders— former VPs of life science technology groups, clinical informatics professionals from top-tier organizations, and a former Big Four consultant—each episode features candid conversations with the people making AI work inside the healthcare enterprise

health & fitness medicine

All Episodes

00:55:07

Sir Peter Donnelly of Genomics: Genetics as Nature's...

Practical AI in Healthcare ·

2026/10/04

Ninety-five percent of drug targets fail, and Professor Sir Peter Donnelly thinks the reason is simple: we can't run experiments on humans. Genetics is the next best thing. People who carry natural variants in a gene show what happens when that gene is dialed up or down over a lifetime. Peter, CEO and co-founder of Genomics, joins Steve Labkoff and Leon Rozenblit to explain how his company harmonized tens of thousands of genetic studies into one resource, what genetics revealed about "good"

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00:55:11

Travis Zack: How OpenEvidence Picks and Grades the...

Practical AI in Healthcare ·

2026/09/27

Doctors hallucinate too. That's the provocation Travis Zack, MD, PhD, chief medical officer of OpenEvidence, brings to this conversation with Steve Labkoff and Leon Rozenblit. A physician with 20 patients and no time to look things up goes with their gut, and can get it wrong in the same ways a language model does. Travis explains why OpenEvidence was built on retrieving evidence rather than generating answers, what makes one set of references better than another, how it grades the strength of

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00:54:15

S2E3 - Randi Seigel & Jared Augenstein: Healthcare AI...

Practical AI in Healthcare ·

2026/09/20

By Manatt’s count, more than 280 bills to regulate AI in healthcare were introduced in 2026, and almost none of the resulting law has been tested in court. Randi Seigel, a partner at Manatt who leads its health care regulatory practice, and Jared Augenstein, a senior managing director at Manatt Health, track every one of them. They join Steve Labkoff and Leon Rozenblit to map where the states are headed, explain why consent and disclosure rules often miss the mark, describe how regulation can

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00:55:28

S2E2 - Greg Raiz: Data-Driven Early-Stage Investing

Practical AI in Healthcare ·

2026/09/13

Early-stage investing is one of the last places where big decisions get made on gut feel. Greg Raiz, co-founder and general partner of the pre-seed fund Founders Edge, is trying to change that with data: a survey of more than 3,000 entrepreneurs and a review of some 42 published studies on what predicts founder success. In a solo conversation with Leon Rozenblit, Greg explains why venture capital may be backing the wrong age group, why angel groups are structured to kill their best deals, who

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00:49:18

S2E1 - Raihan Faroqui, MD: The Doctor at the Front...

Practical AI in Healthcare ·

2026/09/06

Raihan Faroqui trained in internal medicine and then went to work on the front desk. As VP of partnerships at Confido Health, he puts AI agents on the phone lines of what he says are about 1,500 outpatient practice sites, handling appointments, refills, insurance checks, and billing questions in twenty languages. His framing is that healthcare never had a software problem so much as a system of record problem, and agents are the first tool that does not demand a migration. In this conversation, we

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00:48:09

S1, E52 - Reflections #8: Year in Review

Practical AI in Healthcare ·

2026/08/30

Steve and Leon close Season 1 by looking back across all fifty-two episodes. They start with what surprised them (how fast conservative institutions adopted, and how slowly AI literacy is moving), then work through five conclusions a year of guests kept reaching independently: the models are no longer the hard part, almost nobody monitors these systems after deployment, an interface built for a novice can degrade an expert, "compared to what" is the question everyone skips, and money is

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00:56:19

S1, E51 - Reflections #7: When the Machine Is Almost...

Practical AI in Healthcare ·

2026/08/23

In their seventh Reflections episode, and their fifty-first overall, Steve and Leon look back across six conversations: Mika Newton on interoperability that finally started working, Peter Embi on monitoring clinical AI after deployment, Vimla Patel on how clinicians actually reason, Renee Deehan on an engine built so it can't hallucinate, Christine Dymek on AI literacy, and Jeremy Harper on an adoption curve that broke. They decide against forcing a single grand lesson and find three threads

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00:53:04

S1, E50 - Jeremy Harper: The Broken Adoption Curve,...

Practical AI in Healthcare ·

2026/08/16

Every technology healthcare has adopted moved through the same curve: a bleeding-edge few went first and documented what broke, and the majority followed. Large language models skipped that entirely. Jeremy Harper, a biomedical informatician who has worked at Epic, Ohio State, and Regenstrief, and who wrote Large Language Models (LLMs) for Healthcare, explains what we gave up by going all at once. Ambient scribes are everywhere, and because most vendors discard the audio as soon as they transcribe

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00:50:06

S1, E49 - Chris Dymek: Healthcare AI Literacy

Practical AI in Healthcare ·

2026/08/09

Chris Dymek came to healthcare informatics by way of philosophy, and she still thinks like a philosopher: the first question is what we actually mean. As Director of Digital Healthcare Research at AHRQ, she funded AI research, helped launch work on AI and patient safety, and wrote a request for information asking healthcare organizations how they thought about AI literacy. It was never published. She left in May 2025 and carried the work into the DCI network instead. In this conversation, she lays

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00:50:44

S1, E48 - Renee Deehan: Trustworthy AI for Consumer...

Practical AI in Healthcare ·

2026/08/02

The internet is full of wellness advice built on a single cherry-picked study. Renee Deehan, a molecular and cell biologist who leads science and AI at InsideTracker, spent two decades building the opposite. In this episode, she explains why the core of their recommendation engine is symbolic AI, knowledge representation and reasoning, rather than a large language model: it's deterministic, fully auditable, and by design cannot hallucinate. The LLMs are fenced off to chat and summaries, while

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00:54:05

S1, E47 - Vimla Patel: Cognitive Science of Clinical...

Practical AI in Healthcare ·

2026/07/26

Dr. Vimla Patel has spent four decades studying how physicians actually reason, and what happens when technology ignores it. In this episode, she explains the difference between forward reasoning (the fast, pattern-driven hallmark of expertise) and backward reasoning (the slower, hypothesis-testing mode of novices), and why most clinical AI is built for the wrong one. Through two vivid cases, a textbook expert diagnosis and a near-fatal potassium overdose driven by a flawed order-entry system, she

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00:57:46

S1, E46 - Peter Embi: The Doctor Who Diagnosed Himself

Practical AI in Healthcare ·

2026/07/19

Peter Embi has spent his career at the intersection of medicine and informatics: coining the term "algorithmovigilance," serving as the nation's first Chief Research Information Officer, and leading AI work at Vanderbilt. He is also a patient. A rare adrenal tumor took nearly 15 years and a self-diagnosis to catch, and it nearly killed him. In this conversation, Embi connects that diagnostic odyssey to the case for monitoring clinical AI the way we monitor drugs: continuously, in the

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00:53:42

S1, E45 - Mika Newton, xCures: Intelligent...

Practical AI in Healthcare ·

2026/07/12

Interoperability has been healthcare's twenty-year broken promise, but something genuinely changed. Mika Newton, CEO of xCures, explains how provider data exchange jumped from roughly 30% to 85–90% in just two years, and why that's only half the story. Moving records, it turns out, was never the hard part. As Newton puts it, "records travel, but they don't translate" — most of a record arrives as duplicated notes and scanned images that still have to be made usable. Steve and

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00:48:44

S1, E44 - Reflections #6: After Five Episodes, What...

Practical AI in Healthcare ·

2026/07/05

In their sixth Reflections episode, Steve and Leon look back across five conversations (Sarah Rossetti, Jeff Smith of ONC, Hugo Campos, Fred Bennett, and Zak Kohane) and decide, deliberately, not to force a single grand theme. What surfaces anyway is one question asked five ways: as AI gets genuinely capable, what still has to be human, and what makes it trustworthy? They cover Rossetti's CONCERN system that models the nurse instead of the patient, ONC giving AI agents patient-like data rights,

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00:57:44

S1, E43 - Isaac "Zak" Kohane, MD, PhD (Chair,...

Practical AI in Healthcare ·

2026/06/28

A quiet war is underway over who controls your health data in the age of AI, and the front-runners aren't the usual suspects. Dr. Isaac "Zak" Kohane, Chair of Biomedical Informatics at Harvard Medical School and founding editor of NEJM AI, traces the line from SMART on FHIR (the accidental standard he helped create in 2009) to today's battle for the doctor-facing AI layer, where companies like OpenEvidence are outrunning the EHR incumbents. He also unveils his Human Values Project,

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00:58:32

S1, E42 - Live with Fred Bennett, Founder & CEO,...

Practical AI in Healthcare ·

2026/06/21

For its first-ever live episode, recorded before an audience at New York Tech Week, Practical AI in Healthcare sits down with Fred Bennett, founder and CEO of PatientTalker — an ambient-AI app built for the patient rather than the clinician. (Steve Labkoff is a disclosed advisor to the company.) Bennett traces the idea to his father's cardiology visit, where three family members left with three different memories of the same conversation. The discussion covers why patients are the forgotten

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00:51:37

S1, E41 - Hugo Campos: Patient-Directed AI

Practical AI in Healthcare ·

2026/06/14

Patient advocate Hugo Campos spent more than a decade fighting for access to the data from his own implanted defibrillator. When the system wouldn't budge, he stopped trying to reform it and started building around it. In this episode, Hugo shows how he used agentic AI coding tools to create OpenKP, an open-source app that liberates his records from inside Kaiser Permanente, despite calling himself a non-coder. He and the hosts unpack the line between institutional AI and patient-directed AI,

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00:43:40

S1, E40 - Jeff Smith — AI Regulation, Transparency &...

Practical AI in Healthcare ·

2026/06/07

What happens when the rules for getting AI into clinical care are written by someone who has spent his career inside both the advocacy world and the government? In this episode, we talk with Jeff Smith of ONC at HHS, the first government official on Practical AI in Healthcare. Smith walks us through ONC's proposed HTI-5 rule, including a striking move to treat AI agents as "users" with the same data-access rights as clinicians, and a new question about whether blocking data from being

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00:53:05

S1, E39 - Sarah Rossetti, RN, PhD: Nursing...

Practical AI in Healthcare ·

2026/05/31

On National Nurses Day, Practical AI in Healthcare welcomes its first nurse: Sarah Rossetti, RN, PhD, of Columbia University. Her CONCERN early warning system takes an unusual approach to predicting patient deterioration. Instead of modeling a patient's vital signs and labs, it models the nurse's documentation behavior, since the frequency and timing of charting reflect clinical concern long before the numbers move. In a 74-unit randomized trial of more than 60,000 patients, published in

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00:34:55

S1, E38 - Reflections 5: How Specialized Does AI Have...

Practical AI in Healthcare ·

2026/05/24

In their fifth Reflections episode, Steve and Leon look back across six conversations (Matt Truppo at Sanofi, Ted Shortliffe, Barry Chaiken, David Hidalgo-Gato, and Danny van Leeuwen) to ask a sharper question: how specialized does AI have to be to actually work? The throughline is depth. The LLM is a commodity, and so, increasingly, is the generalist agent. What stays scarce is specialization in a workflow, the revival of symbolic methods like knowledge graphs, the literacy that separates an

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