Healthcare AI accountability has shifted to the deploying organization. The FDA’s 2024 guidance on lifecycle management of AI-enabled devices, HHS HTI-1’s transparency requirements, ACA Section 1557’s algorithmic-discrimination provisions, and the 2025–2026 wave of state AI-impersonation laws all land on the deploying organization — the hospital, payer, or digital-health company that puts the AI in front of a patient — rather than the model vendor. And the failure modes that produce enforcement risk are the ones most testing programs miss. A clinical task benchmark misses demographic bias. A bias audit misses cognitive bias and sycophancy. A red-team exercise misses regulatory readiness. A privacy scan misses adolescent-confidentiality and child-safety failures. None of it runs continuously in production, where models drift, RAG corpora change, and new regulations take effect every quarter.
We’ll walk through 60+ healthcare-specific test suites spanning seven categories: clinical decision support, general medical knowledge, documentation and patient communication, research and administration, safety and robustness, cognitive and demographic bias, and social bias. The suites draw on peer-reviewed open-source sources including MedHELM, HealthBench, LangTest, and ChildSafeLLM — and on proprietary suites Pacific AI built where no public equivalent existed, including clinical cognitive bias, several demographic and social bias datasets, and targeted clinical safety probes. Every suite is clinician-reviewed, traceable to its source publication or methodology, and mapped to the 250+ regulations, frameworks, and standards in the Pacific AI Policy Suite, refreshed quarterly as new legislation takes effect.
What you’ll learn:
- Which production failure modes — clinical, fairness, safety, regulatory — generic LLM benchmarks structurally miss, and why they surface only under healthcare-specific testing.
- How the test library is organized across the seven categories and the specific suites in each.
- How the same suites run as a pre-release CI/CD gate (Gatekeeper) and continuously against deployed systems (Guardian), throttled to near-zero production impact.
- What “good” looks like for a healthcare AI testing program in 2026: continuous testing at the same cadence as production deployment, owned by the system team rather than a central committee, with quarterly re-testing against the Policy Suite
We’ll also present a live demo highlighting comprehensive testing and monitoring across pre-release and production environments. You’ll see a clinical case run through demographic perturbations with fairness scores recomputed live on each run, then watch the same test suite execute as a pre-release CI/CD gate and as a throttled probe against a live production endpoint. We’ll close the demo by publishing test results directly into a CHAI-compliant model card, with no manual authoring.
