Practical AI in Healthcare

Episodes

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  Adam Blum Returns: One Record Instead of Many Extracts 01.11.2026 48m

  Adam Blum returns to talk about what sits underneath his clinical-trial matcher: PRomop, an open-source patient record built on OMOP. Now CTO of HealthKey.ai, which acquired CancerBot this year, Adam argues that organizations can replace their transactional, analytics, and decision-ready stores with one, and that a single flat record per disease is enough. Leon Rozenblit pushes back on whether one record can serve many questions, and Steve Labkoff presses on staleness and where the AI actually lives. Steve advises HealthKey and co-authored the PRomop paper with Adam.
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  Charles (Charlie) Safran, MD, MS: 50 Years of AI in Medicine 25.10.2026 54m

  In 1973, Charlie Safran wrote programs for an MIT group that expected a computer doctor by the year 2000. Fifty years later, the former chief of clinical informatics at Beth Israel Deaconess and past AMIA chair walks Steve Labkoff and Leon Rozenblit through what changed and what didn't: Bayesian decision models for Hodgkin's disease, rules of care in early electronic health records, why those rules were so hard to maintain, and why clinical data will always reflect what gets paid for. His conclusion: reasoning turned out to be the easier part, and technology should make care more human, not less.
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  Reflections #9: Where Healthcare AI Actually Is 18.10.2026 55m

  Six conversations into Season 2, Steve Labkoff and Leon Rozenblit look back at what they added up to. They revisit Raihan Faroqui on AI agents at the front desk, Greg Raiz on data over gut feel in pre-seed investing, Randi Seigel and Jared Augenstein on the state-by-state map of AI law, Travis Zack on an evidence engine that grades its own sources, Sir Peter Donnelly on genetics as nature's drug trial, and Latha Valluripalli Soorya and Erica Crawford on smart glasses in the autism evaluation. Their read: the field is moving from hype into implementation, efficiency keeps arriving before accuracy, and "AI" may be too broad a word to regulate or even to argue about.
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  Latha Valluripalli Soorya & Erica Crawford: Smart Glasses in Autism Evaluation 11.10.2026 51m

  Families who suspect autism can wait up to two years for an evaluation. Dr. Latha Valluripalli Soorya, a clinical psychologist at Rush University Medical Center and co-founder of Argus Cognitive, and Dr. Erica Crawford, Clinical Director at Autism Testing 4 Kids in Dallas–Fort Worth, join Steve Labkoff and Leon Rozenblit to discuss Neurora: smart glasses a clinician wears during a standard autism assessment, with software that measures a child's gaze, facial movement and speech. AT4K was a reimbursed site in its FDA study. They cover what the clinician gets back, the hard cases where the data helps, what the published study does and doesn't show, and why the biggest time savings so far came from report writing.
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  Sir Peter Donnelly of Genomics: Genetics as Nature's Drug Trial 04.10.2026 55m

  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" cholesterol, why the AI agent sits on top rather than at the core, and what genetics still can't tell a drug developer.
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  Travis Zack: How OpenEvidence Picks and Grades the Evidence 27.09.2026 55m

  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 evidence behind an answer in real time, why health systems and EHR vendors act as gatekeepers, and why the last mile of a clinical decision still belongs to a human.
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  Randi Seigel & Jared Augenstein: Healthcare AI Law 20.09.2026 54m

  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 penalize companies that tried to do the right thing, and offer practical advice on AI governance for hospitals with one-person committees and academic systems with too many.
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  Greg Raiz: Data-Driven Early-Stage Investing 13.09.2026 55m

  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 actually gets the leverage from AI coding tools, and why electronic medical records are ripe for disruption. Leon is a limited partner in Founders Edge and discloses it at the top of the episode.
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  Raihan Faroqui, MD: The Doctor at the Front Desk, AI on the Line 06.09.2026 49m

  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 get into the economics of the unanswered phone call, why the deployment model looks more like consulting than software, how they define and measure agent failure, and the question the episode keeps circling: these agents do not tell patients they are agents.
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  S1, E50 - Jeremy Harper: The Broken Adoption Curve, Ambient Scribes Nobody Can Audit, and the Knowledge We Keep Paying For and Deleting 02.09.2026 53m

  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 it, nobody can say how often the notes are wrong. He offers a fix borrowed from de-identification, a one-question test for any AI vendor, and a lament for the reusable knowledge we keep paying for and deleting.
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  S1, E52 - Reflections #8: Year in Review 30.08.2026 48m

  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 usually the real gate. They also notice the show has started talking to itself, with guests answering each other across dozens of episodes. Several questions are left open on purpose and handed to Season 2.
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  S1, E51 - Reflections #7: When the Machine Is Almost Always Right, Who Is Still Thinking? 23.08.2026 56m

  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 anyway: keeping a human in the loop as a deliberate design decision, the national clearinghouse for AI errors that still doesn't exist, and whether literacy is even the right word for what healthcare workers need.
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  S1, E49 - Chris Dymek: Healthcare AI Literacy 09.08.2026 50m

  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 out the distinction at the center of her framework, knowing-that versus knowing-how, the three constituencies who each need something different, and why a literate staff and a literate patient population are what let a health system move forward without breaking things.
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  S1, E48 - Renee Deehan: Trustworthy AI for Consumer Health 02.08.2026 50m

  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 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.
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  S1, E47 - Vimla Patel: Cognitive Science of Clinical Reasoning 26.07.2026 54m

  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 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.
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  S1, E46 - Peter Embi: The Doctor Who Diagnosed Himself 19.07.2026 57m

  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 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.
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  S1, E45 - Mika Newton, xCures: Intelligent Interoperability 12.07.2026 53m

  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 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.
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  S1, E44 - Reflections #6: After Five Episodes, What Still Has to Be Human? 05.07.2026 48m

  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, 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.
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  S1, E43 - Isaac "Zak" Kohane, MD, PhD (Chair, Biomedical Informatics, Harvard Medical School; founding Editor-in-Chief, NEJM AI) 28.06.2026 57m

  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, 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.
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  S1, E42 - Live with Fred Bennett, Founder & CEO, PatientTalker 21.06.2026 58m

  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 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.