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This research addresses the need for a dedicated channel for distributing clinical guidance to AI systems, reducing misinformation risks.

May 19, 2026Open Access

Sovereignty by Design

Key Points

This research addresses the need for a dedicated channel for distributing clinical guidance to AI systems, reducing misinformation risks.
Identified current issues in clinical guidance distribution and AI ingestion.
Proposed five regulatory instruments to ensure accountability and accuracy.
Mapped the gap between AI Act obligations and clinical AI deployment realities.
Outlined five instruments for improving clinical guidance sovereignty, including registries and attestation feeds.
Identified operational needs to maintain regulatory compliance in AI system outputs.
Provided a framework that bridges high-level governance principles and practical AI application.

Abstract

Guideline authorities (WHO, NICE, HAS, NEJM, BMJ, JAMA) currently distribute clinicalguidance as PDFs and web pages. General-purpose large language models ingest, paraphrase,and re-emit this guidance at a conversational scale — stripped of provenance, version, andjurisdiction. This paper names that failure mode "provenance erosion" and argues thatclinical guidance now needs its own distribution rail: a signed, versioned, machine-A queryable channel through which authoritative content can be retrieved by AI systemsunder the issuing body's control. The paper is addressed to regulators (EU AI Act competent authorities, FDA DigitalHealth Center of Excellence, MHRA, Health Canada) and to the guideline authoritiesthemselves. It maps the gap between current AI-Act obligations (Articles 9-15, 50;Annex III §5) and the operational reality of clinical AI deployments, and proposesfive concrete instruments — guideline sovereignty registries, attestation feeds,binding refusal taxonomies, audit-grade trails, and bilateral conformity assessment —that turn high-level governance principles into deployable infrastructure. The companion technical paper, "The Refusal Stack" (Mastari, 2026,doi:10.5281/zenodo.20257894), provides the engineering primitives — refusal classes,retrieval-bound generation, prompt-injection containment — that makes the policyThe framework in this paper enforceable. Part of a three-paper series on defendable medical AI infrastructure published byCLINETHIX (2026).

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