In this episode of Practical AI in Healthcare, we sit down with Dr. Jeff Chuang, a computational biologist at The Jackson Laboratory, to explore how AI is reshaping cancer diagnostics, starting with pediatric sarcoma. Jeff shares his journey from physics and protein folding to computational pathology, where machine learning is being applied to standard H&E pathology slides to deliver faster, cheaper, and more accurate diagnoses.

The conversation dives into how AI models trained on relatively small but carefully curated image datasets can outperform traditional diagnostic approaches, especially in rare cancers where expertise is scarce. We also explore the challenges of data sharing, IRB approvals, and real-world deployment, along with a glimpse into the future of spatial genomics and ultra-high-resolution tissue analysis. This episode is a powerful example of how practical AI can directly improve patient care today.

In this episode of Practical AI in Healthcare, we sit down with physician–informaticist Josh Geleris, MD, co-founder and Chief Product Officer of SmarterDx, to unpack one of healthcare’s most overlooked AI opportunities: revenue cycle intelligence. Drawing on his clinical training, deep technical background, and firsthand experience inside large health systems, Josh explains how AI can bridge the gap between clinical reality and billing documentation. The conversation explores how machine learning and large language models translate thousands of data points from an inpatient stay into accurate, compliant coding, helping health systems reduce revenue leakage while staying firmly within regulatory guardrails. From SQL queries to post-trained LLMs, Josh walks us through the evolution of SmarterDx’s AI stack and why human-in-the-loop design remains essential. This is a grounded, practical look at AI delivering real value where healthcare operations and clinical truth collide.

In this episode of Practical AI in Healthcare, we sit down with Dr. Alvin Liu, retinal surgeon and Professor of Artificial Intelligence and Ophthalmology at Johns Hopkins University, to explore one of the earliest and most successful real-world deployments of medical AI.

Dr. Liu walks us through the evolution of autonomous AI for diabetic retinopathy screening, from FDA approval to large-scale clinical implementation across health systems. We unpack what it really takes to move AI from validation to impact, including workflow integration, sensitivity and specificity tradeoffs, reimbursement challenges, and post-market monitoring. The conversation also looks ahead to emerging AI applications using retinal imaging to predict cardiovascular disease, dementia, and kidney disease at the population level.

This episode is a masterclass in how AI can meaningfully improve access, equity, and outcomes in healthcare when deployed thoughtfully and responsibly.

• In this episode of Practical AI in Healthcare, we sit down with Dr. Tiffany Leung, Scientific Editorial Director at JMIR Publications, to explore how artificial intelligence is reshaping scientific publishing from the inside out. As open access journals face unprecedented volumes of submissions, AI is simultaneously enabling faster discovery and creating new challenges around research integrity, peer review, and trust in the scientific record.

• 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

Spotify: https://open.spotify.com/show/63NBSNTdKsLHO7jmNxiZHv?si=277e9a14c24f41d9

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

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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.