Practical AI in Healthcare

Amy Price, brain injury survivor and participatory medicine researcher who leads the Journal of Participatory Medicine, discusses why patients should shape AI design. She recounts recovery, using tech as a cognitive prosthesis, and co-produced research with teens. She highlights training teams, practical AI use for personal health decisions, closing the patient AI literacy gap, and building inclusive AI governance.

Shashi Shankar, co-founder and CEO of Novellia and former Genentech executive, built a patient-centered data solution after a family cancer journey. He explains how patient-authorized SMART on FHIR records create longitudinal real-world data. The conversation covers why past personal health record attempts failed, how AI spots clinical data errors, and the tradeoffs of trusting Big Tech with patient information.

Adam Blum, a serial AI entrepreneur who built CancerBot after his follicular lymphoma diagnosis, describes building precision clinical-trial matching tools. He explains the Prompt Workbench for high-accuracy extraction. He unpacks transforming messy eligibility into tractable logic using conjunctive normal form. He discusses patient flows, accuracy results, and open-source collaborations.

For 37 years, Charlie Harp heard the same thing from healthcare organizations: "Our data quality is fine." They were right — for billing and scheduling. But AI changed the equation. Harp, founder of Clinical Architecture, built the PIQI framework to measure patient data quality across four dimensions: availability, accuracy, conformance, and plausibility. His PIQXL Gateway scores data on a 1-100 scale before it enters your systems — not after. Early deployments reveal uncomfortable truths: lab data averages 70% quality against USCDI standards, and one facility coded every blood test to a single LOINC code. The framework is now going through HL7 balloting as an open national standard.

Every day, patients leave US hospitals with discharge instructions they can't read. Giovanni Donatelli, CEO of The Language Group, built FETCH — a patented AI system embedded in Epic that translates discharge documents in 15 minutes with human review. He did it because he was the 8-year-old interpreting for his immigrant parents at doctor's appointments. Hosts Steve Labkoff and Leon Rozenblit explore the discharge instruction gap, the tragic cases that make it personal, FETCH's three-layer translation pipeline, the case for keeping humans in the loop, and why healthcare executives think they've already solved a problem that doesn't yet have a solution.

They debate why their framework failed to capture recent stories and where their views diverged. They explore how unglamorous AI wins like billing can succeed while reimbursement often blocks practical impact. Legal and privacy rules already apply to AI and create liability and de-identification challenges. They warn about AI-enabled paper mills threatening the scientific record and discuss editorial guardrails.

Bob Wachter, MD, UCSF Chair of Medicine and author of The Digital Doctor, reflects on why AI could outpace a failing healthcare system. He discusses Watson's missteps, the rise of ambient scribes, human-in-the-loop design challenges, AI literacy for patients, and the need to compare AI to current alternatives. Short, provocative, and focused on practical change.

What if early signs of psychosis could be detected from how patients speak—not what they say, but how they organize their thoughts?Amar Mandavia (VA Boston, Boston University) and Enrique "Kike" Gutiérrez (Polytechnic University of Madrid) join hosts Steve Labkoff and Leon Rozenblit to discuss CHiRP, an AI tool that identifies formal thought disorder from routine clinical conversations. They explain why the gold-standard manual test takes 5+ hours, how their system reduces that to minutes, and the hard ethical questions around labeling patients as "at risk."Key topics: prodromal psychosis detection, NLP in mental health, clinical workflow integration, MIT linQ Catalyst, and the payer challenges that make prevention hard to fund.

Real-world evidence was supposed to accelerate drug development. Instead, we've created definitional chaos—over 100 data vendors, inconsistent definitions, and studies that can't be compared.Dr. Aaron Kamauu, CEO of Navidence and co-host of Real World Wednesday, explains why one missing diagnosis code can exclude 30% of your cohort, how GLP-1 eligibility criteria vary wildly between NHS and US guidelines, and what it means to document "the seven definitions you chose NOT to use."A conversation about the unsexy infrastructure that makes evidence trustworthy.

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.