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Testing Clinical LLMs for Cognitive Bias - Pacific AI Webinar on continuous testing and monitoring of LLMs in healthcare explores accuracy, fairness, safety, and compliance with Pacific AI governance tools.

Healthcare organizations are deploying AI faster than they can govern it.

A modern health system can have more than 100 AI systems in flight, many introduced by individual departments long before central governance catches up.

Forms-and-templates governance simply doesn't scale. Frontier models also carry measurable bias and reliability gaps that generic benchmarks miss.

The challenge is no longer writing another policy.

It is building an operating model that continuously governs, tests, and monitors AI across the entire portfolio.

That is the focus of our upcoming hands-on workshop with John Snow Labs.

You'll work through the complete healthcare AI governance lifecycle:

• Register AI systems and vendors in Governor
• Automate AI risk assessments and model cards
• Test models for safety, bias, robustness, and cognitive bias in Gatekeeper
• Monitor production systems for accuracy, bias, safety, and drift in Guardian
• Map 250+ laws, regulations, and industry standards using the AI Policy Suite

The workshop includes hands-on exercises on the CHAI-certified Pacific AI platform, concluding with a certification exam and deployment in your own AWS or Azure tenant.

If your organization is preparing to operationalize healthcare AI governance rather than simply document it, this workshop is designed for you.

Registration details: https://www.eventbrite.com/e/healthcare-ai-governance-testing-and-monitoring-tickets-1996959072342

Small, Private, and First on All Fifteen: The New Medical LLM Benchmark Results First on all 15 clinical and biomedical benchmarks, averaging 80.9 against the newest frontier releases, running on a single GPU inside your own environment.

Every new AI law forces governance teams to revisit the policies and controls behind every affected system.

Pacific AI’s free AI Governance Policy Suite brings together 250+ laws, regulations, frameworks, and standards across the US, EU, and 30+ other countries. It includes nine organisational policies covering the AI lifecycle, risk, safety, privacy, fairness, transparency, incident reporting, copyright, and acceptable use.

Use it as the baseline for your AI governance programme. Customise the policies for your organisation, formally adopt them, and connect them to the testing and monitoring processes required to make governance operational. The suite is free for internal use and updated quarterly.

Download the AI Policy Suite:
https://pacific.ai/ai-policies/

The Joint Commission's Responsible Use of AI in Healthcare (RUAIH) certification covers 5 areas: governance and AI registry, data management, risk and bias reduction, monitoring and validation, and transparency and training.

Governance means a functioning registry of AI systems with documented risk tiers and model cards. Risk and bias reduction means pre-release testing before systems touch patients. Monitoring means continuous tracking of accuracy, bias, and drift in production. Transparency means documented disclosures to staff and patients.

Pacific AI's RUAIH Certification Readiness engagement maps to each area: Governor for registry and model cards, Gatekeeper for pre-release testing, Guardian for production monitoring, and the AI Policy Suite for documentation and disclosure requirements.

Pacific AI is a CHAI-certified Assurance Resource Provider. Coalition for Health AI (CHAI) co-developed the RUAIH framework with the Joint Commission.

If your organization is assessing readiness, we offer a scoping call to identify gaps and build a 12-week plan.

No existing Joint Commission accreditation is required to apply.

Learn more and schedule a scoping call: https://pacific.ai/advisory-managed-services/

Three Healthcare AI Frameworks, one governance backbone: RUAIH, URAC, and CHAI - Pacific AI U.S. healthcare now has two AI certifications and a set of governance playbooks, but they all rest on one backbone you must sustain. In about a year, U.S. healthcare went from having no shared way to govern AI to having two certifications and a detailed set of governance playbooks. URAC published it...

A leaderboard rank is not a clinical evaluation.

It shows how a model performed on a curated dataset. It does not show whether that model can draft a defensible discharge summary, support a differential diagnosis, or perform a medication calculation inside your EHR.
The gap matters because procurement decisions depend on evidence that extends beyond leaderboard performance.

Three questions to ask vendors before you sign:
• What was this model evaluated on? Real EHR data or exam-style question banks?
• Which clinical tasks were tested, and how do they map to the workflows you are actually deploying?
• Can the evaluation be reproduced inside your environment, or only on the vendor's claims?

If those questions cannot be answered, the governance evidence is incomplete before the model goes live.

Background on MedHELM and why healthcare AI evaluation extends beyond leaderboard rankings:https://pacific.ai/medhelm-and-the-next-phase-of-open-source-medical-ai-evaluation/

Why Healthcare AI Agents Can Be Right for the Wrong Reason A healthcare AI agent can reach the right answer for the wrong reas...