Healthcare AI Has a Missing Layer: The Evidence Gap
Healthcare AI doesn’t have a model problem—it has a proof problem.
AI in AgeCare Isn’t Failing—We’re Failing to Prove It Works
AI in AgeCare isn’t failing because the technology is weak—it’s failing because we haven’t proven it works. From poor validation to lack of standardization, the real gap isn’t intelligence. It’s trust.
From Principles to Proof: Why Healthcare AI Needs More Than Guidelines — and How CognitiveView is Operationalizing CHAI
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: From Generative AI Hype to Agentic AI Governance
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.
AI Human Impact Signals (AI-Human)
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 Isn’t About Location — It’s About Defensible Deployment Decisions
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.
AI Ships in Weeks. Governance Delays Deals for Months
AI Agents Are the New Employees — But Who Manages Them?
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
Operationalizing AI Assurance: Turning Evaluation into Evidence
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 Is No Longer Enough: How HAIGS Builds Trust in Healthcare AI
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.
Why Responsible AI Needs TRACE — Operational Evidence, Not Just Policies
TRACE is an open assurance framework that turns Responsible AI from intent into evidence. It links model metrics to legal clauses, automates controls, and delivers audit-ready proof—without black-box platforms.
Doctor, This AI Model Is Safe—Here’s the Proof
Radiology AI tools are powerful—but are they provably safe? This post explores how TRACE transforms performance metrics into HIPAA-compliant audit logs and factsheets for patients and clinicians alike.