Practical AI in Healthcare

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

Practical AI in Healthcare is a health podcast hosted by Unknown Host, with 56 episodes on record and a Required Pod Score of 80.

Unknown Host hosts Practical AI in Healthcare, a health show with 56 episodes published.

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  Episode #5

  Peter Donnelly of Genomics: Genetics as Nature's Drug Trial

  Oct 4, 202655 minS2

  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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  Episode #3

  S2E3 - Randi Seigel & Jared Augenstein: Healthcare AI Law

  Sep 20, 202654 minS2

  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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  Episode #2

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

  Sep 13, 202655 minS2

  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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  Episode #1

  S2E1 - Raihan Faroqui, MD: The Doctor at the Front Desk, AI on the Line

  Sep 6, 202649 minS2

  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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  Practical AI in Healthcare

  S1, E52 - Reflections #8: Year in Review

  Aug 30, 202648 min

  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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