Solving the Grand Challenges of Healthcare AI: The Trust Stack for the Regulatory-Grade Era

What it actually takes to build regulatory-grade AI in healthcare, from raw clinical data to audited production systems. David Talby breaks down the “Trust Stack” required to move AI from pilots to real clinical deployment. David Talby (CEO, John Snow Labs) opens Applied Healthcare AI Summit 2026 with a deep dive into one of the hardest problems in healthcare AI: how to move from fragmented tools and demos to trustworthy, auditable, production-grade systems. This keynote introduces the concept of the “Regulatory-Grade Trust Stack”, a practical architecture connecting data preparation, AI development, and continuous validation into a single scalable framework. ⏱️ Key moments: 00:00 Why healthcare AI fails to reach production 00:58 The missing layers: data preparation and validation 02:24 The Trust Stack: connecting data, AI, and governance 03:51 The data accuracy gap: why structured data is not enough 06:05 Engineering and governance gaps in real-world systems 09:32 Building a unified patient data layer (Patient Journey Intelligence) 14:59 AI governance in practice: Governor, Gatekeeper, Guardian 18:44 Cost and ROI: why enterprise AI needs a different model The session also explores how Patient Journey Intelligence and Pacific AI enable continuous validation, monitoring, and compliance for healthcare AI systems in production. This is how healthcare AI actually gets deployed in regulated environments. 📌 Applied Healthcare AI Summit 2026 — what actually works in real-world healthcare AI, from pilots to production systems. #HealthcareAI #AIinHealthcare #AIGovernance #ClinicalAI #LLM