0.1.0 - ci-build

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

Home

Official URL: http://example.org/fhir/eu-ai-transparency/ImplementationGuide/fhir.ig.eu.aitransparency Version: 0.1.0
Draft as of 2026-07-31 Computable Name: EUAITransparencyIG

EU AI Transparency Implementation Guide

Overview

This Implementation Guide (IG) defines a custom FHIR R5 framework for representing selected AI-related transparency, traceability, legal-context, and human-oversight metadata in healthcare.

The IG focuses on how documentation requirements and transparency-relevant concepts from the EU AI Act, the GDPR, and the European Health Data Space (EHDS) can be represented using machine-readable FHIR artifacts. It provides profiles, extensions, terminology, and examples for documenting AI-supported processing in clinical contexts.

The IG does not claim to provide complete legal compliance or regulatory certification. Instead, it supports structured documentation, traceability, and interoperability for selected AI-related metadata.

Purpose

AI-supported healthcare workflows require technical documentation that is understandable, traceable, and interoperable across systems. Relevant information may include the identity of the AI system, its intended purpose, technical documentation, training-data context, privacy metadata, legal processing context, generated outputs, execution traces, human oversight, and patient-facing information.

This IG provides a FHIR-based representation of these concepts by defining reusable profiles and extensions. The goal is to make selected AI-related metadata explicit, structured, and linkable within healthcare IT environments.

Scope

The IG covers selected metadata areas relevant to AI-supported processing in healthcare:

  • AI system identification and system-level metadata
  • Organizational accountability and contact information
  • Model-card and technical-documentation metadata
  • Training-data and data-quality context
  • Privacy and data-use metadata
  • AI-generated clinical outputs
  • Execution traceability and audit metadata
  • Provenance and legal-context documentation
  • Human oversight actions
  • Patient-facing information and explanation documentation

The IG does not replace clinical validation, conformity assessment, data protection assessment, national legal review, or organization-specific governance processes.

Architecture

The Implementation Guide is organized into three main architectural contexts:

  • Static System Context
  • AI Output and Execution Context
  • Clinical Decision and Patient-Facing Context

Detailed descriptions of all profiles are available in the Profiles section.

Contents

This Implementation Guide contains:

  • Profiles
  • Extensions
  • Code Systems
  • Value Sets
  • Example Instances
  • Downloads
  • Dependency Information

Author: Selina Adlberger
Context: Developed as part of a Master's Thesis at the University of Applied Sciences Upper Austria (Hagenberg).