Healthcare AI Governance Library - Pacific AI

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- - - Governor
      Centralized registry for systems, vendors, risks, model cards and policies across the AI lifecycle.
      
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      Automate LLM, ML, and Agentic testing. Run pre-release test suites as a CI/CD release gate.
      
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      Monitor model performance, detect bias, and protect against adversarial attacks in real-time.
      
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- - - AI Policy Suite
      Access and utilize our curated collection of comprehensive, ready-to-deploy AI governance and safety policies.
  - - MedHELM
      A comprehensive Stanford CRFM benchmarking project, built to evaluate LLMs on real-world clinical tasks.
  - - LangTest
      A comprehensive, unified testing library for measuring language model accuracy, bias, and robustness in LLM applications.
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How the Pacific AI Governance Policy Suite Aligns with U.S. Federal Anti-Discrimination Laws

How the Pacific AI Governance Policy Suite Aligns with U.S. Federal Anti-Discrimination Laws

Abstract digital illustration of secured data blocks, analytics charts, and a privacy shield with EU stars, representing how organizations manage privacy risks in large language models to support responsible AI practices and GDPR compliance.

Managing Privacy Risks in Large Language Models: Guidance for Responsible AI and GDPR Compliance

Conceptual illustration of a central AI oversight eye connected to audit reports, data cards, and performance metrics, symbolizing a responsible AI audit that evaluates transparency, risk, compliance, and governance across AI systems.

What is a Responsible AI Audit?

Illustration of a cloud-based governance dashboard representing governance for generative AI, showing centralized policies, compliance controls, documentation, and oversight mechanisms for managing generative AI systems responsibly.

What Is Governance for Generative AI?

Illustration of a healthcare AI assistant using a laptop, representing an introduction to generative AI governance in healthcare, with emphasis on responsible AI use, clinical oversight, data governance, and compliance in medical settings.

Introduction to Generative AI Governance in Healthcare

Portrait of a healthcare data science leader alongside the article title, illustrating testing for bias in large language models used in clinical applications, with a focus on fairness, patient safety, and responsible AI in healthcare.

Testing for Bias of Large Language Models in Clinical Applications

Speaker portraits with the article title “Automating AI Governance for Healthcare Applications of Generative AI,” highlighting expert discussion on healthcare AI governance, regulatory compliance, risk management, and responsible deployment of generative AI systems.

Automating AI Governance for Healthcare Applications of Generative AI

Speaker portraits with the article title “Automating AI Governance for Healthcare Applications of Generative AI,” highlighting expert discussion on healthcare AI governance, regulatory compliance, risk management, and responsible deployment of generative AI systems.

AI Governance Simplified: Unifying 70+ laws, regulations, and standards Into a Policy Suite

Shield icon labeled “AI Governance Certified,” representing Pacific AI’s launch of a free AI policy suite designed to address AI legal risk, governance certification, and regulatory compliance for organizations deploying AI systems.

Pacific AI Launches to Tackle Growing AI Legal Risks with a Free AI Policy Suite

Healthcare AI safety frameworks illustrated by a secure lock and key, surrounded by labels such as FDA, WHO, CHAI, CLAIM, CRAFT-MD, and STARD-AI, representing regulatory and evaluation standards for trustworthy clinical AI.

Healthcare AI Laws: A Review of Evaluation Frameworks – Part 1

Visual overview of AI regulation updates for Q1 2025, showing a digital brain connected to global policies including U.S. AI regulations, California AI rules, executive orders, the EU AI Act, and the UK AI plan, reflecting Pacific AI regulatory release notes.

AI Regulation Updates for Q1 2025: Pacific AI Release Notes

Illustration of large language model robustness testing using LangTest in Databricks, showing an AI brain connected to evaluation metrics, test cases, and data pipelines for validating LLM reliability and performance.

Robustness Testing of LLM Models Using LangTest in Databricks

Holistic Evaluation of Large Language Models: Assessing Robustness, Accuracy, and Toxicity for Real-World Applications

LangTest evaluation workflow for custom LLM and NLP models, showing automated testing pipelines, dataset augmentation, and before-and-after performance metrics for safety, robustness, and model quality assessment.

LangTest: A comprehensive evaluation library for custom LLM and NLP models

Identifying and mitigating bias in AI recruiting models, featuring data science and HR technology experts discussing fairness, transparency, and responsible AI practices in hiring and talent selection systems.

Identifying and Mitigating Bias in AI Models for Recruiting

Automated testing of bias, fairness, and robustness in generative AI solutions, highlighting responsible AI evaluation with expert insights on model reliability, risk detection, and governance-ready validation.

Automated Testing of Bias, Fairness, and Robustness of Generative AI Solutions

Building responsible language models with the LangTest library, illustrating automated testing for bias, robustness, and safety in large language models to support trustworthy and governance-ready AI systems.

Building Responsible Language Models with the LangTest Library

Ethical implications of medical large language models in healthcare, showing clinical data flows, model decision layers, and governance controls to address transparency, safety, and responsible AI use.

The Ethical Implications of Medical LLMs in Healthcare

LangTest evaluation workflow for custom LLM and NLP models, showing automated testing pipelines, dataset augmentation, and before-and-after performance metrics for safety, robustness, and model quality assessment.

LangTest: Unveiling & Fixing Biases with End-to-End NLP Pipelines

Automated testing framework for detecting demographic bias in clinical treatment plans generated by large language models, highlighting secure medical documents, validation checks, and responsible AI evaluation in healthcare.

Automatically Testing for Demographic Bias in Clinical Treatment Plans Generated by Large Language Models

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