John Snow Labs 58d ago

You can build a great healthcare AI model — and still not be allowed to use it. The real problem is not accuracy, but proving safety, privacy, and control in production systems. ⏱️ Key moments: 00:00 Why trust, not models, is the real bottleneck 02:25 Trust is a system property, not just a model feature 06:20 Privacy by design: PHI boundaries and control 11:50 Proof gates: evaluation, thresholds, and safety checks 14:30 Human-in-the-loop and risk-based routing 18:20 Platform thinking: scaling trust across teams Neel Gandhi (Machine Learning Software Engineer at Google) breaks down how to move from isolated AI demos to production-ready systems that are auditable, observable, and safe to deploy. Instead of focusing only on model accuracy, this talk introduces a system-level approach to trust in healthcare AI: — Privacy by design with explicit PHI boundaries and modality-aware controls — Performance you can prove through evaluation frameworks, thresholds, and traceability — Platform thinking that enables reuse of governance, validation, and monitoring across teams The key idea is simple: trust is not a model feature — it is a system property that must be engineered. 📌 Applied Healthcare AI Summit 2026 — what actually works in real-world healthcare AI, from pilots to production systems. #HealthcareAI #AIGovernance #TrustworthyAI #ClinicalAI #DataPrivacy #HumanInTheLoop #AIInfrastructure #MLOps