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Practical AI in Healthcare: Daily Summaries Delivered
by Steven Labkoff
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.
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- Today54 min
S2E3 - Randi Seigel & Jared Augenstein: Healthcare AI Law
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.
- 1 weeks ago55 min
S2E2 - Greg Raiz: Data-Driven Early-Stage Investing
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.
- 2 weeks ago49 min
S2E1 - Raihan Faroqui, MD: The Doctor at the Front Desk, AI on the Line
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.
- 3 weeks ago48 min
S1, E52 - Reflections #8: Year in Review
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.
- 4 weeks ago56 min
S1, E51 - Reflections #7: When the Machine Is Almost Always Right, Who Is Still Thinking?
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.
- Aug 16, 202653 min
S1, E50 - Jeremy Harper: The Broken Adoption Curve, Ambient Scribes Nobody Can Audit, and the Knowledge We Keep Paying For and Deleting
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.
- Aug 9, 202650 min
S1, E49 - Chris Dymek: Healthcare AI Literacy
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.
- Aug 2, 202650 min
S1, E48 - Renee Deehan: Trustworthy AI for Consumer Health
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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About 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.
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