From requirements to verified decisions
Identify the applicable requirement, select an authoritative domain reference, test for contradiction or omission, and record the approved disposition.
PUBLIC WHITE PAPER / SEPTEMBER 2026The Assurance-to-
The Assurance-to-
Runtime Gap.
Nextvise’s 19-page white paper explains how regulated institutions move from applicable requirements to verified AI decisions: checking consequential outputs against controlled terminology and authoritative sources, then enforcing the approved response at runtime. Examples cover clinical assertions, pharmacovigilance, precision medicine and GxP manufacturing.
Download white paper (PDF)An approved system is not enough. The consequential output must also be supported, controlled and reconstructable.
HOW IT WORKS
Inside the white paper.
The paper connects regulatory requirements to domain-grounded verification and a reproducible runtime disposition, supported by a reference matrix to primary regulatory and technical sources.
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Healthcare and life-sciences verification
Examples cover clinical assertions, pharmacovigilance, precision medicine and pharmaceutical manufacturing, grounded in controlled terminology and sources.
Deterministic runtime controls
Apply Approve, Escalate or Block conditions, route uncertain cases to qualified people, and preserve the source, control and accountable action.
WHITE PAPER FAQ
How does regulated AI verification work?
Answers drawn from the paper’s operating model and reference matrix.
What is the assurance-to-runtime gap?
It is the gap between governing an AI system before deployment and verifying that each consequential output is supported by the domain facts and controls required for its intended use.
How does the paper describe verification of a regulated AI output?
It links an applicable requirement to an authoritative domain reference, tests for contradiction, omission or unsupported claims, then applies an approved outcome: Approve, Escalate or Block.
Can deterministic runtime controls replace qualified human judgment?
No. The paper directs uncertain, conflicting or consequential cases to a qualified professional when judgment is needed. Deterministic controls make approved rules reproducible; they do not make medicine, law or science mechanically certain.
Which regulated workflows does the white paper cover?
Examples include clinical assertions, pharmacovigilance, precision medicine and pharmaceutical manufacturing. Its reference matrix includes sources such as EMA GVP Module VI, MedDRA, RxNorm, ClinVar, FDA guidance, the EU AI Act, ISO/IEC 42001 and NIST AI RMF.
Does the white paper certify that an AI system is compliant?
No. The paper describes its control reference matrix as a navigation aid, not legal advice. Applicability depends on role, jurisdiction, intended use and the specific decision.
PUBLICATION / FREE PDFThe Assurance-to-
Download white paper The Assurance-to-
Runtime Gap.
Read the full public white paper, including the regulatory reference matrix. No registration required.