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AI in healthcare is no longer a model problem — it’s a trust problem. As frameworks like CHAI define what Responsible AI should look like, healthcare leaders face a harder question: Can we prove AI is safe for real-world care?
HIMSS 2026 signals a shift from generative AI hype to operational AI governance. Explore how agentic AI, continuous monitoring, and Responsible AI frameworks are shaping the future of healthcare technology.
Artificial Intelligence is becoming decision infrastructure, yet human consequences remain difficult to observe continuously. Why AI governance must evolve from system evaluation to human impact observability.
Sovereign AI is often reduced to where models are hosted or data is stored. But sovereignty isn’t proven by geography. It’s proven by whether AI deployment decisions can be explained, justified, and defended over time.
AI Ships in Weeks. Governance Delays Deals for Months
Most AI failures don’t start with bad models. They start with unclear go-live decisions.
New York’s latest AI laws aren’t just about regulating technology. They’re about making trust visible, enforceable, and provable — from frontier models to synthetic humans.
CognitiveView extends RSA Archer’s AI Governance with continuous AI assurance
A new class of digital workers is entering the enterprise. Governance — not hype — will determine who succeeds. On a recent call with a CIO at a large healthcare network, she described a moment that caught her entire leadership team off guard. Their AI agent, designed to help with documentation and
AI metrics alone don’t satisfy regulators. Learn how to operationalize AI assurance by converting evaluations into audit-ready evidence aligned with EU AI Act, NIST RMF, and ISO 42001.
Compliance alone won’t earn patient trust in healthcare AI. Passing audits is not enough—outcomes, fairness, and transparency matter most. This blog with IAIGH CEO Josh Baker explores how the HAIGS framework helps providers move from box-ticking compliance to demonstrable trust.
HITRUST’s AI Risk Management Assessment shifts healthcare compliance from policy checklists to continuous model monitoring. Learn why metrics like bias, drift, and explainability now matter—and how TRACE helps map these metrics directly to HITRUST controls for real-time, audit-ready evidence.
