AI Transparency on FHIR
1.0.0-current - STU1 Ballot
AI Transparency on FHIR
1.0.0-current - STU1 Ballot
AI Transparency on FHIR, published by HL7 International / Electronic Health Records. This guide is not an authorized publication; it is the continuous build for version 1.0.0-current built by the FHIR (HL7® FHIR® Standard) CI Build. This version is based on the current content of https://github.com/HL7/aitransparency-ig/ and changes regularly. See the Directory of published versions
| Page standards status: Trial-use | Maturity Level: 2 |
@prefix fhir: <http://hl7.org/fhir/> .
@prefix owl: <http://www.w3.org/2002/07/owl#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
# - resource -------------------------------------------------------------------
<http://hl7.org/fhir/CodeSystem/AIdeviceTypeCS> a fhir:CodeSystem ;
fhir:nodeRole fhir:treeRoot ;
fhir:Resource.id [ fhir:value "AIdeviceTypeCS"] ;
fhir:Resource.meta [
fhir:Meta.security [
fhir:index -1 ;
fhir:Coding.system [ fhir:value "http://terminology.hl7.org/CodeSystem/v3-ObservationValue" ] ;
fhir:Coding.version [ fhir:value "5.0.0" ] ;
fhir:Coding.code [ fhir:value "AIAST" ] ;
fhir:Coding.display [ fhir:value "Artificial Intelligence asserted" ]
]
] ;
fhir:Resource.language [ fhir:value "en"] ;
fhir:DomainResource.text [
fhir:Narrative.status [ fhir:value "generated" ] ;
fhir:Narrative.div "<div xmlns=\"http://www.w3.org/1999/xhtml\"><p class=\"res-header-id\"><b>Generated Narrative: CodeSystem AIdeviceTypeCS</b></p><a name=\"AIdeviceTypeCS\"> </a><a name=\"hcAIdeviceTypeCS\"> </a><div style=\"display: inline-block; background-color: #d9e0e7; padding: 6px; margin: 4px; border: 1px solid #8da1b4; border-radius: 5px; line-height: 60%\"><p style=\"margin-bottom: 0px\"/><p style=\"margin-bottom: 0px\">Security Label: <a href=\"http://terminology.hl7.org/7.2.0/CodeSystem-v3-ObservationValue.html\">Artificial Intelligence asserted (Details: ObservationValue code AIAST = 'Artificial Intelligence asserted')</a></p></div><p><b>Properties</b></p><p><b>This code system defines the following properties for its concepts</b></p><table class=\"grid\"><tr><td><b>Name</b></td><td><b>Code</b></td><td><b>URI</b></td><td><b>Type</b></td></tr><tr><td>Not Selectable</td><td>abstract</td><td>http://hl7.org/fhir/concept-properties#notSelectable</td><td>boolean</td></tr></table><p><b>Concepts</b></p><p>This case-sensitive code system <code>http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS</code> defines the following codes in a Grouped By hierarchy:</p><table class=\"codes\"><tr><td><b>Lvl</b></td><td style=\"white-space:nowrap\"><b>Code</b></td><td><b>Display</b></td><td><b>Definition</b></td><td><b>Not Selectable</b></td></tr><tr><td>1</td><td style=\"white-space:nowrap\">AI-By-Scenario<a name=\"AIdeviceTypeCS-AI-By-Scenario\"> </a></td><td>Classification by Application Scenario</td><td>This category classifies AI systems based on their application scenarios in the medical field.</td><td>true</td></tr><tr><td>2</td><td style=\"white-space:nowrap\"> Intelligent-Diagnosis-and-Treatment<a name=\"AIdeviceTypeCS-Intelligent-Diagnosis-and-Treatment\"> </a></td><td>Intelligent Diagnosis and Treatment</td><td>By analyzing massive volumes of medical data, these AI systems assist doctors in making more accurate diagnostic and treatment decisions.</td><td/></tr><tr><td>2</td><td style=\"white-space:nowrap\"> Medical-Image-Analysis<a name=\"AIdeviceTypeCS-Medical-Image-Analysis\"> </a></td><td>Medical Image Analysis</td><td>Leveraging deep learning technologies, these AI tools automatically identify lesion areas in medical images.</td><td/></tr><tr><td>2</td><td style=\"white-space:nowrap\"> Personalized-Treatment<a name=\"AIdeviceTypeCS-Personalized-Treatment\"> </a></td><td>Personalized Treatment</td><td>These AI systems create precise patient profiles to formulate personalized treatment plans.</td><td/></tr><tr><td>2</td><td style=\"white-space:nowrap\"> Drug-Discovery-and-Development<a name=\"AIdeviceTypeCS-Drug-Discovery-and-Development\"> </a></td><td>Drug Discovery and Development</td><td>AI in this category accelerates the screening of candidate drugs and optimizes the design of clinical trials.</td><td/></tr><tr><td>2</td><td style=\"white-space:nowrap\"> Medical-Quality-Control<a name=\"AIdeviceTypeCS-Medical-Quality-Control\"> </a></td><td>Medical Quality Control</td><td>These AI tools are used to generate standardized medical document templates and detect defects in medical documents and images.</td><td/></tr><tr><td>2</td><td style=\"white-space:nowrap\"> Patient-Services<a name=\"AIdeviceTypeCS-Patient-Services\"> </a></td><td>Patient Services</td><td>AI systems here provide patients with services such as intelligent medical guidance, symptom self-assessment, and medical consultation.</td><td/></tr><tr><td>1</td><td style=\"white-space:nowrap\">AI-By-DataType<a name=\"AIdeviceTypeCS-AI-By-DataType\"> </a></td><td>Classification by Processed Data Type</td><td>This category classifies AI systems based on the types of medical data they primarily process.</td><td>true</td></tr><tr><td>2</td><td style=\"white-space:nowrap\"> AI-for-Medical-Imaging-Data<a name=\"AIdeviceTypeCS-AI-for-Medical-Imaging-Data\"> </a></td><td>AI for Medical Imaging Data</td><td>It mainly processes medical imaging data such as X-rays, MRIs, and CT scans.</td><td/></tr><tr><td>2</td><td style=\"white-space:nowrap\"> AI-for-Physiological-Signal-Data<a name=\"AIdeviceTypeCS-AI-for-Physiological-Signal-Data\"> </a></td><td>AI for Physiological Signal Data</td><td>This type of AI deals with physiological signal data like electrocardiograms (ECG) and electroencephalograms (EEG).</td><td/></tr><tr><td>2</td><td style=\"white-space:nowrap\"> AI-for-Medical-Text-Data<a name=\"AIdeviceTypeCS-AI-for-Medical-Text-Data\"> </a></td><td>AI for Medical Text Data</td><td>It processes text data such as electronic health records (EHRs) and medical abstracts.</td><td/></tr><tr><td>1</td><td style=\"white-space:nowrap\">AI-By-Model<a name=\"AIdeviceTypeCS-AI-By-Model\"> </a></td><td>Classification by Technical Model</td><td>This category classifies AI systems based on the underlying technical models they employ.</td><td>true</td></tr><tr><td>2</td><td style=\"white-space:nowrap\"> Machine-Learning-Models<a name=\"AIdeviceTypeCS-Machine-Learning-Models\"> </a></td><td>Machine Learning Models</td><td>They include supervised learning models (e.g., Support Vector Machines (SVM), Random Forests (RF)), which can be used for disease classification and risk prediction; unsupervised learning models (e.g., K-means clustering), which can discover hidden characteristics of patient subgroups; and reinforcement learning models, which can be applied in dynamic treatment plan management.</td><td/></tr><tr><td>2</td><td style=\"white-space:nowrap\"> Deep-Learning-Models<a name=\"AIdeviceTypeCS-Deep-Learning-Models\"> </a></td><td>Deep Learning Models</td><td>Examples include Convolutional Neural Networks (CNNs), which perform excellently in medical image analysis; Recurrent Neural Networks (RNNs) and their variant LSTMs, which are suitable for processing time-series physiological signal data; Generative Adversarial Networks (GANs), which can be used to synthesize training data and alleviate the scarcity of medical data; and Transformer models, which are widely used in multiple tasks such as medical imaging, text analysis, and physiological signal prediction.</td><td/></tr><tr><td>2</td><td style=\"white-space:nowrap\"> Large-Language-Models<a name=\"AIdeviceTypeCS-Large-Language-Models\"> </a></td><td>Large Language Models</td><td>These models, such as GPT-4 and PaLM, are trained on massive text datasets and can perform various natural language processing tasks, including medical text understanding, generation, and question answering.</td><td/></tr><tr><td>2</td><td style=\"white-space:nowrap\"> Hybrid-Models<a name=\"AIdeviceTypeCS-Hybrid-Models\"> </a></td><td>Hybrid Models</td><td>These models combine multiple AI techniques to leverage their respective strengths. For instance, combining CNNs and RNNs can effectively process medical image sequences; integrating machine learning and deep learning models can enhance disease prediction accuracy; and combining rule-based systems with machine learning can improve interpretability and reliability in clinical decision support.</td><td/></tr><tr><td>1</td><td style=\"white-space:nowrap\">Artificial-Intelligence<a name=\"AIdeviceTypeCS-Artificial-Intelligence\"> </a></td><td>All kinds of Artificial Intelligence</td><td>Any type of Artificial Intelligence system, undifferentiated.</td><td/></tr></table></div>"
] ;
fhir:DomainResource.extension [
fhir:index -1 ;
fhir:Extension.url [ fhir:value "http://hl7.org/fhir/StructureDefinition/structuredefinition-wg" ] ;
fhir:Extension.valueCode [ fhir:value "ehr" ]
], [
fhir:index -1 ;
fhir:Extension.url [ fhir:value "http://hl7.org/fhir/StructureDefinition/structuredefinition-fmm" ] ;
fhir:Extension.valueInteger [
fhir:value "2"^^xsd:integer ;
fhir:Element.extension [
fhir:index -1 ;
fhir:Extension.url [ fhir:value "http://hl7.org/fhir/StructureDefinition/structuredefinition-conformance-derivedFrom" ] ;
fhir:Extension.valueCanonical [
fhir:value "http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency" ;
fhir:link <http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency>
]
]
]
], [
fhir:index -1 ;
fhir:Extension.url [ fhir:value "http://hl7.org/fhir/StructureDefinition/structuredefinition-standards-status" ] ;
fhir:Extension.valueCode [
fhir:value "trial-use" ;
fhir:Element.extension [
fhir:index -1 ;
fhir:Extension.url [ fhir:value "http://hl7.org/fhir/StructureDefinition/structuredefinition-conformance-derivedFrom" ] ;
fhir:Extension.valueCanonical [
fhir:value "http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency" ;
fhir:link <http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency>
]
]
]
] ;
fhir:CodeSystem.url [ fhir:value "http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS"] ;
fhir:CodeSystem.identifier [
fhir:index -1 ;
fhir:Identifier.system [ fhir:value "urn:ietf:rfc:3986" ] ;
fhir:Identifier.value [ fhir:value "urn:oid:2.16.840.1.113883.4.642.40.79.16.1" ]
] ;
fhir:CodeSystem.version [ fhir:value "1.0.0-current"] ;
fhir:CodeSystem.name [ fhir:value "AIdeviceTypeCS"] ;
fhir:CodeSystem.title [ fhir:value "Device type for Artificial Intelligence"] ;
fhir:CodeSystem.status [ fhir:value "active"] ;
fhir:CodeSystem.experimental [ fhir:value "false"^^xsd:boolean] ;
fhir:CodeSystem.date [ fhir:value "2026-07-23T15:08:33+00:00"^^xsd:dateTime] ;
fhir:CodeSystem.publisher [ fhir:value "HL7 International / Electronic Health Records"] ;
fhir:CodeSystem.contact [
fhir:index -1 ;
fhir:ContactDetail.name [ fhir:value "HL7 International / Electronic Health Records" ] ;
fhir:ContactDetail.telecom [
fhir:index -1 ;
fhir:ContactPoint.system [ fhir:value "url" ] ;
fhir:ContactPoint.value [ fhir:value "http://www.hl7.org/Special/committees/ehr" ]
], [
fhir:index -1 ;
fhir:ContactPoint.system [ fhir:value "email" ] ;
fhir:ContactPoint.value [ fhir:value "ehr@lists.hl7.org" ]
]
] ;
fhir:CodeSystem.description [ fhir:value "This CodeSystem contains codes for the Device.type that indicate that the Device is an AI. The codes here were created by AI."] ;
fhir:CodeSystem.jurisdiction [
fhir:index -1 ;
fhir:CodeableConcept.coding [
fhir:index -1 ;
fhir:Coding.system [ fhir:value "http://unstats.un.org/unsd/methods/m49/m49.htm" ] ;
fhir:Coding.code [ fhir:value "001" ]
]
] ;
fhir:CodeSystem.caseSensitive [ fhir:value "true"^^xsd:boolean] ;
fhir:CodeSystem.hierarchyMeaning [ fhir:value "grouped-by"] ;
fhir:CodeSystem.content [ fhir:value "complete"] ;
fhir:CodeSystem.count [ fhir:value "17"^^xsd:nonNegativeInteger] ;
fhir:CodeSystem.property [
fhir:index -1 ;
fhir:CodeSystem.property.code [ fhir:value "abstract" ] ;
fhir:CodeSystem.property.uri [ fhir:value "http://hl7.org/fhir/concept-properties#notSelectable" ] ;
fhir:CodeSystem.property.type [ fhir:value "boolean" ]
] ;
fhir:CodeSystem.concept [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "AI-By-Scenario" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Classification by Application Scenario" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "This category classifies AI systems based on their application scenarios in the medical field." ] ;
fhir:CodeSystem.concept.property [
fhir:index -1 ;
fhir:CodeSystem.concept.property.code [ fhir:value "abstract" ] ;
fhir:CodeSystem.concept.property.valueBoolean [ fhir:value "true"^^xsd:boolean ]
] ;
fhir:CodeSystem.concept.concept [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Intelligent-Diagnosis-and-Treatment" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Intelligent Diagnosis and Treatment" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "By analyzing massive volumes of medical data, these AI systems assist doctors in making more accurate diagnostic and treatment decisions." ]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Medical-Image-Analysis" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Medical Image Analysis" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "Leveraging deep learning technologies, these AI tools automatically identify lesion areas in medical images." ]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Personalized-Treatment" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Personalized Treatment" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "These AI systems create precise patient profiles to formulate personalized treatment plans." ]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Drug-Discovery-and-Development" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Drug Discovery and Development" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "AI in this category accelerates the screening of candidate drugs and optimizes the design of clinical trials." ]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Medical-Quality-Control" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Medical Quality Control" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "These AI tools are used to generate standardized medical document templates and detect defects in medical documents and images." ]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Patient-Services" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Patient Services" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "AI systems here provide patients with services such as intelligent medical guidance, symptom self-assessment, and medical consultation." ]
]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "AI-By-DataType" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Classification by Processed Data Type" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "This category classifies AI systems based on the types of medical data they primarily process." ] ;
fhir:CodeSystem.concept.property [
fhir:index -1 ;
fhir:CodeSystem.concept.property.code [ fhir:value "abstract" ] ;
fhir:CodeSystem.concept.property.valueBoolean [ fhir:value "true"^^xsd:boolean ]
] ;
fhir:CodeSystem.concept.concept [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "AI-for-Medical-Imaging-Data" ] ;
fhir:CodeSystem.concept.display [ fhir:value "AI for Medical Imaging Data" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "It mainly processes medical imaging data such as X-rays, MRIs, and CT scans." ]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "AI-for-Physiological-Signal-Data" ] ;
fhir:CodeSystem.concept.display [ fhir:value "AI for Physiological Signal Data" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "This type of AI deals with physiological signal data like electrocardiograms (ECG) and electroencephalograms (EEG)." ]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "AI-for-Medical-Text-Data" ] ;
fhir:CodeSystem.concept.display [ fhir:value "AI for Medical Text Data" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "It processes text data such as electronic health records (EHRs) and medical abstracts." ]
]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "AI-By-Model" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Classification by Technical Model" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "This category classifies AI systems based on the underlying technical models they employ." ] ;
fhir:CodeSystem.concept.property [
fhir:index -1 ;
fhir:CodeSystem.concept.property.code [ fhir:value "abstract" ] ;
fhir:CodeSystem.concept.property.valueBoolean [ fhir:value "true"^^xsd:boolean ]
] ;
fhir:CodeSystem.concept.concept [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Machine-Learning-Models" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Machine Learning Models" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "They include supervised learning models (e.g., Support Vector Machines (SVM), Random Forests (RF)), which can be used for disease classification and risk prediction; unsupervised learning models (e.g., K-means clustering), which can discover hidden characteristics of patient subgroups; and reinforcement learning models, which can be applied in dynamic treatment plan management." ]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Deep-Learning-Models" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Deep Learning Models" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "Examples include Convolutional Neural Networks (CNNs), which perform excellently in medical image analysis; Recurrent Neural Networks (RNNs) and their variant LSTMs, which are suitable for processing time-series physiological signal data; Generative Adversarial Networks (GANs), which can be used to synthesize training data and alleviate the scarcity of medical data; and Transformer models, which are widely used in multiple tasks such as medical imaging, text analysis, and physiological signal prediction." ]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Large-Language-Models" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Large Language Models" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "These models, such as GPT-4 and PaLM, are trained on massive text datasets and can perform various natural language processing tasks, including medical text understanding, generation, and question answering." ]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Hybrid-Models" ] ;
fhir:CodeSystem.concept.display [ fhir:value "Hybrid Models" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "These models combine multiple AI techniques to leverage their respective strengths. For instance, combining CNNs and RNNs can effectively process medical image sequences; integrating machine learning and deep learning models can enhance disease prediction accuracy; and combining rule-based systems with machine learning can improve interpretability and reliability in clinical decision support." ]
]
], [
fhir:index -1 ;
fhir:CodeSystem.concept.code [ fhir:value "Artificial-Intelligence" ] ;
fhir:CodeSystem.concept.display [ fhir:value "All kinds of Artificial Intelligence" ] ;
fhir:CodeSystem.concept.definition [ fhir:value "Any type of Artificial Intelligence system, undifferentiated." ]
] .
# - ontology header ------------------------------------------------------------
<http://hl7.org/fhir/CodeSystem/AIdeviceTypeCS.ttl> a owl:Ontology ;
owl:imports fhir:fhir.ttl ;
owl:versionIRI <http://build.fhir.org/CodeSystem/AIdeviceTypeCS.ttl> .
IG © 2024+ HL7 International / Electronic Health Records.
Package hl7.fhir.uv.aitransparency#1.0.0-current based on FHIR 4.0.1.
Generated
2026-07-23
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