Industries · Healthcare
Healthcare AI Assurance
Healthcare AI systems carry risks that other sectors do not: diagnostic errors cause direct patient harm, clinical decision support bias creates care disparities, and PHI exposure through model outputs creates HIPAA liability. We provide independent testing and evaluation for healthcare organizations deploying AI in care delivery, administration, and operations.
AI risk in healthcare
Clinical AI systems — diagnostic support, prior authorization, patient risk stratification, and clinical documentation — operate in an environment where failure carries direct consequences for patients and regulatory consequences for deployers. The testing discipline appropriate to these systems is not general-purpose QA: it requires evaluation against the specific failure modes that harm patients and expose organizations.
Healthcare organizations also face HIPAA obligations that extend to AI systems processing protected health information. PHI can be exposed through model memorization, through RAG pipelines that surface records to unauthorized users, or through model outputs that reconstruct identifying information. These are engineering problems requiring adversarial testing, not documentation problems requiring policy review.
Clinical AI bias — systematic differences in model performance across patient populations — is a patient safety issue and a potential regulatory issue under Section 1557 of the Affordable Care Act. Bias evaluation in healthcare AI requires appropriate demographic stratification and an understanding of the clinical context in which disparities matter.
Services most relevant to healthcare
LLM Red-Teaming →
PHI extraction testing, prompt injection in clinical applications, and system prompt security for patient-facing AI.
AI Model Evaluation →
Bias and fairness assessment across patient populations, robustness evaluation, and drift monitoring for clinical models.
AI Observability Engineering →
HIPAA-aligned logging and audit trail design for AI systems processing PHI.
AI Governance Advisory →
Policy and oversight structure for clinical AI deployment and ongoing monitoring.
Regulatory context
Healthcare AI operates within a layered regulatory environment. HIPAA governs PHI handling for covered entities and business associates, including AI vendors who process patient data. Section 1557 of the Affordable Care Act prohibits discrimination in health programs, which increasingly includes algorithmic discrimination in clinical decision support. Clinical AI systems used in diagnosis or treatment may be subject to FDA regulation as software as a medical device.
The NIST AI RMF and emerging HHS guidance provide frameworks for AI risk management in healthcare contexts, though specific requirements continue to develop. We track these developments and calibrate our work to the current regulatory landscape rather than outdated frameworks.
Frequently asked questions
What AI risks are specific to healthcare?
Healthcare AI carries risks that other sectors do not face at the same severity: diagnostic errors can cause direct patient harm, bias in clinical decision support can create disparities in care, and PHI exposure through model outputs may create HIPAA liability. Additionally, clinical AI systems used in certain diagnostic or treatment decision contexts may be regulated as medical devices under FDA authority, adding a regulatory layer beyond general AI governance considerations.
Does HIPAA apply to AI systems?
HIPAA applies to covered entities and their business associates. If an AI system processes, stores, or transmits protected health information, the vendor and deployer must meet HIPAA requirements. AI-specific risks — such as a model that memorizes and can reproduce PHI from training data, or a RAG pipeline that surfaces PHI to unauthorized users — are not explicitly addressed in HIPAA but fall within the framework's existing requirements. AI deployment should be reviewed against HIPAA obligations at the system design stage, not after deployment.
What is clinical AI bias and why does it matter?
Clinical AI bias occurs when a model performs systematically differently across patient populations — by race, sex, age, or other demographic characteristics. This can occur because training data underrepresents certain groups, because proxy variables encode demographic correlates, or because the clinical environment where the model was trained differs from the deployment environment. Biased clinical AI can contribute to health disparities and may create liability under Section 1557 of the Affordable Care Act, which prohibits discrimination in health programs.