Practical AI in Healthcare

Tiffany shares how journals are adapting to generative AI tools, from policy development and disclosure norms to editorial decision support systems that help identify potential risks without stifling innovation. The conversation moves beyond hype to examine how AI can act as a co scientist, streamline editorial workflows, and potentially redefine peer review itself. This episode offers a rare look at how AI is influencing not just what gets published, but how knowledge is validated and shared globally.

In this episode, Steven Labkoff and Leon Rozenblit explore the legal, regulatory, and risk-management challenges introduced by AI with two top experts in the field: Kathy Roe, Principal at Health Law Consultancy, and Kenny White, Director of the Managed Care Industry Group at Alliant Insurance Services.

Together, they unpack how AI intersects with medical malpractice, product liability, HIPAA privacy, de-identification, intellectual property, contractual risk, and insurance coverage. Kathy and Kenny explain why AI is not yet the standard of care — but why clinicians and health systems must develop AI literacy now as the legal landscape evolves.

A must-listen episode for healthcare leaders navigating the risks and realities of AI adoption.

In our second Reflections episode, Steve Labkoff and Leon Rozenblit synthesize the most meaningful insights from guests across Episodes 9–15. Drawing on conversations with Orr Inbar, Martin Leach, Ing Ho, Yuri Quintana, and patient advocate E-Patient Dave, this episode highlights the themes shaping practical AI adoption in today’s healthcare landscape.

Key topics include the inflection point AI has created across clinical care, research, and patient engagement; the need for stronger data stewardship to support trustworthy automation; and the emerging promise of AI-driven clinical trial optimization and simulation. We also explore how patients are engaging with AI tools independently, raising new questions about literacy, safety, and empowerment.

Additional themes include the cultural alignment required for tools like ambient listening and chart summarization to succeed and what it will take for AI to avoid the missteps of past health-IT transitions.

The episode closes with a preview of upcoming guests discussing AI in law, payer innovation, psychiatric diagnostics, and the future of scientific publishing.

We just released one of the most intellectually energizing conversations we’ve had on Practical AI in Healthcare. Our guest this week is Dr. Adam Rodman, physician, informatician, historian of medicine, and one of the most original thinkers at the intersection of AI and clinical reasoning.

Adam brings a rare perspective to the field: before becoming an AI researcher, he spent years studying how humans make medical decisions — how clinicians reason, where cognition breaks down, and how technology reshapes the way we conceptualize disease. His reflections on the evolution from QMR and INTERNIST-1 to today’s large language models are a must-hear.

In this episode, we explore:
• Why LLMs challenge 50 years of clinical decision support assumptions
• New collaboration models for doctors, patients, and AI — not just “human in the loop,” but truly redesigned workflows
• What’s holding back clinical AI adoption (spoiler: it’s not accuracy)
• The regulatory gap—and why the FDA’s old device mindset won’t work for generative models
• How urgent-care AI companies are early signals of a broader shift

Adam also discusses what the next 3–5 years may actually look like — and why change will be slow…until the moment it becomes very fast.

Listen here and join the conversation on the future of practical, safe, and human-centered AI in healthcare.

Apple Podcasts: https://podcasts.apple.com/us/podcast/practical-ai-in-healthcare/id1837172964

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Amazon Podcasts: https://music.amazon.com/podcasts/8ed0ba60-15f4-419b-85f1-c4b85af81b41/practical-ai-in-healthcare

#HealthcareAI, #ClinicalAI #MedicalInformatics #ClinicalDecisionSupport #AIinMedicine #DigitalHealth #HealthTech #FutureOfHealthcare #GenerativeAI #AIGovernance #DiagnosticExcellence #PatientCareInnovation #HealthcareTransformation #AIThoughtLeadership #PracticalAI

Practical AI in Healthcare just released one of our most eye-opening conversations yet, featuring Orr Inbar, CEO & Co-Founder of QuantHealth.

QuantHealth is pushing the boundaries of what’s possible in clinical development with AI-driven clinical trial simulation. Orr walked us through how modern deep learning, massive biological knowledge graphs, and patient-level real-world data can now simulate clinical trials with 80–90% accuracy — across dozens of indications, modalities, and trial phases.

In our conversation, we cover:

• How QuantHealth models patient-drug interactions at massive scale
• Why trial design is still the most critical (and fixable) failure point in drug development
• How simulation is becoming the new starting point for protocols
• What AI-first trial design could mean for speed, cost, and reducing avoidable trial failures
• Where the field is headed in the next 5–10 years — including the provocative question of how far simulation can replace human trials

Orr also discusses the acceleration of biological data, the maturity of real-world data, and transformer-based AI — the perfect storm that made this moment possible.

This is one of the best deep dives yet into the practical future of drug development.

In the conclusion of our two-part conversation with Dr. Yin Ho, author of Rushing Headlong: Health IT’s Legacy and the Road to Responsible AI, we explore how healthcare can avoid repeating its digital past. Dr. Ho and hosts Dr. Steven Labkoff and Dr. Leon Rozenblit dive into small language models, decision support vs. decision control, the pitfalls of ambient scribing, and why “rage-building” the next generation of EHRs might be the most responsible act of all.

The newest episode of Practical AI in Healthcare features Dr. Yin Ho—physician, entrepreneur, and author of Rushing Headlong: Health IT's Legacy and the Road to Responsible AI.

In Part 1 of our two-part conversation, Dr. Ho joins Dr. Steven Labkoff and Dr. Leon Rozenblit to unpack 25 years of digital health transformation—from the dawn of electronic medical records to the market and policy forces that shaped today’s health IT landscape.

Together, we explore how well-intentioned decisions created a fragmented system that prioritizes billing over care—and why understanding that history is essential to building a responsible AI future.

#HealthcareAI #DigitalHealth #HealthIT #PracticalAIinHealthcare #Podcast

Dr. Yuri Quintana, Director of the Division of Clinical Informatics at Beth Israel Deaconess Medical Center, joins us this week to discuss the DCI Network’s mission: turning AI in healthcare from theory into practice.
The conversation traces DCI’s evolution as a multi-stakeholder “action tank” focused on collaboration, transparency, and patient safety. Quintana recounts the success of the Signal Through the Noise conference and the insights that emerged—chief among them that “mundane AI” (like triage, scheduling, and documentation tools) is quietly creating real value.
The episode also explores topics like AI literacy, governance, transparency (“AI nutrition (model) labels”), and post-market surveillance for clinical AI—ending with a call to action: keeping healthcare innovation grounded in what truly matters to patients.

In this episode of Practical AI in Healthcare, Dr. Steven Labkoff and Dr. Leon Rozenblit speak with ePatient Dave DeBronkart, a globally recognized advocate for patient empowerment.

Dave shares his journey from a life-threatening kidney cancer diagnosis in 2007 to becoming one of the earliest champions of open data, patient access to medical records, and of late, patient use of AI tools to help empower them along their medical journey.

He recounts how the ability to access information, share data, and join online patient communities helped save his life—and how those lessons now translate into how patients use generative AI. Dave highlights stories where AI enabled patients to identify rare diagnoses, manage health conditions, and even prepare for clinical visits.

The discussion examines how AI literacy and trust can empower patients, the ethical boundaries of sharing health data, and the evolution of patient–clinician partnerships in the era of AI.

This week’s episode of Practical AI in Healthcare dives into AI, truth, and the future of data in the government with Dr. Martin Leach, Chief Data Officer at Black Canyon Consulting.

Dr. Martin Leach, PhD, is a data, science, and technology leader with a career spanning some of the most innovative organizations in life sciences and academia. A self-described “data geek,” Martin began his career as a molecular neuropharmacologist before shifting from the bench to data-driven discovery. Over the past two decades, he has held senior roles at Merck, Biogen, Alexion, and AstraZeneca, and was the inaugural Chief Information Officer at the Broad Institute of MIT and Harvard.

Today, Martin serves as Chief Data Officer at Black Canyon Consulting, where he works these organizations on projects that unite bioinformatics, AI, and data federation — from building semantic knowledge graphs to creating “gold-standard” datasets for model validation. His work bridges the worlds of science, computation, and policy, shaping how government and industry can responsibly harness AI to advance biomedical research and healthcare delivery.

From building gold-standard datasets to exploring quantum AI, Martin shares how these projects are shaping the next generation of healthcare data science.