John Snow Labs 58d ago

Most clinical AI risks don’t come from the model — they come from how it’s used. This talk shows how governance-first design makes AI safe, auditable, and usable in practice. ⏱️ Key moments: 00:00 What governance-first AI means in clinical practice 02:30 System architecture: intent classification and guardrails 05:00 Risk stratification and safe interaction routing 07:40 Human-in-the-loop and escalation design 09:30 Validation, auditability, and production readiness In this session, Brian M. Green (Chief AI Officer & Strategist at Health‑Vision AI) breaks down what a governance-first architecture actually looks like in practice — using a clinician coaching system as a concrete example. Instead of relying on model behaviour alone, the system embeds multiple governance layers directly into the application: intent classification, risk stratification, guardrails, audit logging, and human-in-the-loop escalation. The key idea is simple: governance is not a feature — it is infrastructure. From intent taxonomy to real-time risk-based routing, this talk shows how to design AI systems that stay within scope, reduce liability, and remain audit-ready from day one. 📌 Applied Healthcare AI Summit 2026 — what actually works in real-world healthcare AI, from pilots to production systems. #HealthcareAI #AIGovernance #ClinicalAI #ResponsibleAI #AICompliance #HumanInTheLoop #AISafety #DigitalHealth