IP Review

This publication includes IP covered under the following statements.

These codes are excerpted from ASTM Standard, E1762-95(2013) - Standard Guide for Electronic Authentication of Health Care Information, Copyright by ASTM International, 100 Barr Harbor Drive, West Conshohocken, PA 19428. Copies of this standard are available through the ASTM Web Site at www.astm.org.
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- ASTM Signature Type Codes : AuditEvent/sc-01-ai-only-audit-event-ai-execution-001 , AuditEvent/sc-02-validation-audit-event-ai-execution-001 , AuditEvent/sc-03-override-audit-event-ai-execution-001 and AuditEvent/sc-04-correction-exp-audit-event-ai-execution-001
These codes are excerpted from Digital Imaging and Communications in Medicine (DICOM) Standard, Part 16: Content Mapping Resource, Copyright © 2011 by the National Electrical Manufacturers Association.
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- Audit Event ID : AuditEvent/sc-01-ai-only-audit-event-ai-execution-001 , AuditEvent/sc-02-validation-audit-event-ai-execution-001 , AuditEvent/sc-03-override-audit-event-ai-execution-001 and AuditEvent/sc-04-correction-exp-audit-event-ai-execution-001
This material contains content from LOINC . LOINC is copyright © 1995-2020, Regenstrief Institute, Inc. and the Logical Observation Identifiers Names and Codes (LOINC) Committee and is available at no cost under the license . LOINC® is a registered United States trademark of Regenstrief Institute, Inc.
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- LOINC: Observation/sc-01-ai-only-observation-blood-pressure-001 , Observation/sc-01-ai-only-observation-heart-rate-001 ... Show 18 more
This material derives from the HL7 Terminology (THO). THO is copyright ©1989+ Health Level Seven International and is made available under the CC0 designation. For more licensing information see: https://terminology.hl7.org/license.html
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hl7.fhir.us.hai: Healthcare Associated Infection Implementation Guide ®© HL7 | CC0 ( src )

AI Act Art. 14(4)(c) ( src )

Art. 9(2)(a), (c), (g), (h), (i), (j) ( src )

Legal Obligation (Art. 6(1)(c)) ( src )

TrustAITransparencyIG - Local Development build (v0.1.0) built by the FHIR (HL7® FHIR® Standard) Build Tools. See the Directory of published versions ( src )

Vital Interests (Art. 9(2)(c)) ( src )

External References

Type	Reference	Content
web	example.org	https://example.org/model-card/diagnostic-assist
web	fh-ooe.at	https://fh-ooe.at/fhir/trust-ai-transparency/riskassist/model-card
web	fh-ooe.at	https://fh-ooe.at/fhir/trust-ai-transparency/riskassist/technical-documentation
web	nema.org	DICOM Tag Mapping
web	ihe.net	XDS metadata equivalent
web	snomed.info	SNOMED CT Concept Domain Binding
web	www.omg.org	ServD
web	artificialintelligenceact.eu	The requirements of Article 15 of the EU AI Act concerning the accuracy, robustness, and cybersecurity of high-risk AI systems are not represented as computable artifacts, profiles, extensions, or conformance requirements within this Implementation Guide. However, these requirements remain highly relevant for the design, development, deployment, and governance of AI-enabled solutions and should be taken into account when planning and structuring implementation projects. In particular, project teams should consider the need to document and manage performance metrics, system robustness, error handling, resilience measures, and cybersecurity controls in accordance with applicable regulatory obligations. Article 15 applies throughout the lifecycle of high-risk AI systems and requires appropriate levels of accuracy, robustness, and cybersecurity, including protection against AI-specific security threats.
web	www.rcp.ac.uk	The synthetic acute-care scenario is inspired by the NEWS2 . NEWS2 defines a standardized set of routinely measured physiological parameters for assessing acute-illness severity, including respiratory rate, oxygen saturation, supplemental oxygen, systolic blood pressure, pulse rate, level of consciousness or new confusion, and body temperature. These parameters are suitable for the PoC because they can be represented as structured clinical observations and linked to the generated AI output.

Type

Reference

Content

web

example.org

https://example.org/model-card/diagnostic-assist

web

fh-ooe.at

https://fh-ooe.at/fhir/trust-ai-transparency/riskassist/model-card

web

fh-ooe.at

https://fh-ooe.at/fhir/trust-ai-transparency/riskassist/technical-documentation

web

nema.org

DICOM Tag Mapping

web

ihe.net

XDS metadata equivalent

web

snomed.info

SNOMED CT Concept Domain Binding

web

www.omg.org

ServD

web

artificialintelligenceact.eu

The requirements of Article 15 of the EU AI Act concerning the accuracy, robustness, and cybersecurity of high-risk AI systems are not represented as computable artifacts, profiles, extensions, or conformance requirements within this Implementation Guide. However, these requirements remain highly relevant for the design, development, deployment, and governance of AI-enabled solutions and should be taken into account when planning and structuring implementation projects. In particular, project teams should consider the need to document and manage performance metrics, system robustness, error handling, resilience measures, and cybersecurity controls in accordance with applicable regulatory obligations. Article 15 applies throughout the lifecycle of high-risk AI systems and requires appropriate levels of accuracy, robustness, and cybersecurity, including protection against AI-specific security threats.

web

www.rcp.ac.uk

The synthetic acute-care scenario is inspired by the NEWS2 . NEWS2 defines a standardized set of routinely measured physiological parameters for assessing acute-illness severity, including respiratory rate, oxygen saturation, supplemental oxygen, systolic blood pressure, pulse rate, level of consciousness or new confusion, and body temperature. These parameters are suitable for the PoC because they can be represented as structured clinical observations and linked to the generated AI output.

Unattributed Code Systems

Copyright Fragment

Copyright and Registered Trademark Uses

External References

Internal Images