Podcast
Practical AI in Healthcare
Hosted by Steven Labkoff, MD and Leon Rozenblit, JD, PhD · EN
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
43episodes
Episodes
Newest firstAll episodes
S1, E48 - Renee Deehan: Trustworthy AI for Consumer Health
Yesterday00:50:44Summary readyThe 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 humans still write and review every recommendation against convergent clinical evidence. She and the hosts dig into a 20,000-user outcomes study, the discipline of refusing to claim causality, the MCT-oil case where the system decides not to recommend, and how a data-science team grew its own AI literacy instead of hiring it.
SummaryS1, E47 - Vimla Patel: Cognitive Science of Clinical Reasoning
1w ago00:54:05Summary readyDr. 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 shows how good design preserves clinical judgment and bad design erodes it. She closes with a warning about confident AI and the quiet loss of independent thinking.
SummaryS1, E46 - Peter Embi: The Doctor Who Diagnosed Himself
2w ago00:57:46Summary readyPeter 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 real world, across a network of institutions. The discussion runs from zebras and missed diagnoses to VAMOS, the "air traffic control tower" his team built to keep deployed models honest.
SummaryS1, E45 - Mika Newton, xCures: Intelligent Interoperability
3w ago00:53:42Summary readyInteroperability 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 Leon dig into the honest limits of AI parsing, the card-network model behind nationwide exchange, and why the patient is becoming the real access point to their own data.
SummaryS1, E44 - Reflections #6: After Five Episodes, What Still Has to Be Human?
4w ago00:48:44Summary readyIn 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, the tension between patient empowerment and black boxes you don't understand, the Minimum Trustable Product, and Kohane's method for measuring the values a healthcare AI actually acts on. A practical tour of where good is starting to look real.
SummaryS1, E43 - Isaac "Zak" Kohane, MD, PhD (Chair, Biomedical Informatics, Harvard Medical School; founding Editor-in-Chief, NEJM AI)
Jun 2800:57:44Summary readyA 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, which measures the values embedded inside clinical AI models, and warns how easily payers and pharma could tune them. A two-act conversation about freeing data and guarding values.
SummaryS1, E42 - Live with Fred Bennett, Founder & CEO, PatientTalker
Jun 2100:58:32Summary readyFor 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 end-user of clinical AI, how to build a "minimum trustable product," the honest question of who pays for patient-first tools, and why the technology is rarely the hard part.
SummaryS1, E41 - Hugo Campos: Patient-Directed AI
Jun 1400:51:37Summary readyPatient 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, the discipline of having two AIs check each other, and why he believes "critical AI health literacy" now matters more than knowing how to code.https://practicalaiinhealthcare.com/
SummaryS1, E40 - Jeff Smith — AI Regulation, Transparency & Innovation from the Government Perspective
Jun 700:43:40Summary readyWhat 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 written back into the EHR is itself information blocking. We also dig into the limits of what a regulator can actually do, and why the real work is coordination across agencies rather than control from any one of them.https://practicalaiinhealthcare.com/https://www.youtube.com/@PracticalAIinHealthcare
SummaryS1, E39 - Sarah Rossetti, RN, PhD: Nursing Informatics & the CONCERN Early Warning System
May 3100:53:05Summary readyOn 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 Nature Medicine, CONCERN was associated with a 35.6% reduction in instantaneous mortality risk. Rossetti and the hosts unpack the method, the counterintuitive rise in ICU transfers, equity safeguards, and what ambient AI means for the signal.https://practicalaiinhealthcare.com/episodes/#S1E39More on Sarah Rossetti's work: https://www.dbmi.columbia.edu/profile/sarah-collins-rossetti/
Summary