Practical AI in Healthcare

John Halamka, president of Mayo Clinic Platform, joins Steve Labkoff and Leon Rozenblit to ask what large health datasets can answer and where they run out. A registry will record a patient's toenail length; a medical record won't. John makes the case for testing ideas in real-world data before spending on a trial, and Leon presses him on where randomization still has to happen. They also get into governing thousands of AI skills, why Steve hears an echo of MYCIN, and why John thinks people trust large language models too much.

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.

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.

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.

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.

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.

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.

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.

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.

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.