Governance for FDA Submission Contexts
Clinical AI now influences decisions that regulators, surveyors, and patients depend on. FERZ provides deterministic governance with tamper-evident, replayable records, built for environments shaped by FDA PCCP change-control expectations and designed to support HIPAA-aligned deployments.
The Frameworks Already Apply
Healthcare organizations do not operate in a regulatory vacuum when deploying AI. Existing frameworks apply today, and regulators are extending them.
SaMD and the Total Product Lifecycle
FDA guidance on AI-enabled device software functions spans Good Machine Learning Practice, lifecycle management, and Predetermined Change Control Plans that let manufacturers pre-specify permitted change, provided the change control is governed and examinable.
Privacy and Transparency
HIPAA governs privacy and security obligations, but it does not by itself establish an authorization boundary for AI decisions. ONC transparency requirements now reach predictive algorithms in certified health IT. Organizations must extend these frameworks to AI themselves.
Extraterritorial High-Risk Classification
Medical devices and in vitro diagnostics incorporating AI are classified high-risk, layering conformity assessment, documentation, human oversight, and robustness obligations on top of MDR and IVDR, with reach into U.S. organizations serving EU patients.
Decisioning AI and Communications AI
Clinical AI governance runs on two tracks with different regulatory drivers and different internal owners. A single system can span both, and communications use cases are not inherently lower risk.
Clinical and patient safety risk
Clinical decision support, diagnostic AI, treatment recommendations, risk stratification, and triage prioritization. Governed primarily through clinical validation, FDA pathways, and patient safety frameworks.
Privacy and records risk
Patient communication drafts, clinician copilots, discharge summaries, prior authorization assistance, and clinical documentation. Governed primarily through HIPAA, medical records requirements, and professional standards.
Governance is enforced at the authorization boundary: the point where an output becomes an effect-bearing action, and where evidence, not explanations, must exist. In clinical settings, that is the point where a recommendation reaches a clinician, a record, or a workflow. FERZ evaluates the action against codified rules before that point, issues a verdict of ALLOW, DENY, or ABSTAIN, and records a tamper-evident, replayable authorization artifact. ABSTAIN blocks execution unless an authorized human override is recorded, which keeps the clinician in the loop by architecture, not by policy memo.
Evidence That Survives Review
PCCP-shaped evidence
Deterministic replay of governed decisions supports the change-control discipline PCCP contexts expect: what was permitted, under which policy version, on what basis.
Tamper-evident artifacts
Every verdict leaves a tamper-evident, replayable authorization artifact designed for survey, audit, and submission contexts.
Blocked, not assumed safe
When governance conditions are not met, execution is blocked, not allowed by default. Uncertainty resolves to ABSTAIN and a recorded human decision, not to silent execution.
The Executive Guide
AI Governance for Healthcare: Clinical AI Safety, FDA Compliance, and Patient Protection
For Chief Medical Officers, CIOs, Heads of Clinical AI, Compliance Officers, Clinical Informaticists, and Quality and Patient Safety Officers.
Regulatory mapping across FDA SaMD guidance, the QMSR, HIPAA, ONC transparency requirements, and the EU AI Act. A four-tier risk framework, survey readiness with an Examiner Pack template, GenAI controls, board metrics, and a 90-day implementation roadmap. Published under CC BY 4.0.
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