AI Transparency on FHIR
0.1.0 - ci-build
AI Transparency on FHIR
0.1.0 - ci-build
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 0.1.0 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: Draft | Maturity Level: 0 |
{
"resourceType" : "CodeSystem",
"id" : "AIdeviceTypeCS",
"text" : {
"status" : "generated",
"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><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></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/></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Intelligent-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Medical-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Personalized-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Drug-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Medical-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Patient-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></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/></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0AI-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0AI-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0AI-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></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/></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Machine-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Deep-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Large-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Hybrid-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></tr><tr><td>2</td><td style=\"white-space:nowrap\">\u00a0\u00a0Other-AI-Models<a name=\"AIdeviceTypeCS-Other-AI-Models\"> </a></td><td>Other AI Models</td><td>This category includes other AI models not covered above, such as graph neural networks (GNNs) for modeling complex relationships in medical data, and evolutionary algorithms for optimizing treatment plans.</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/></tr></table></div>"
},
"extension" : [
{
"url" : "http://hl7.org/fhir/StructureDefinition/structuredefinition-wg",
"valueCode" : "ehr"
},
{
"url" : "http://hl7.org/fhir/StructureDefinition/structuredefinition-fmm",
"valueInteger" : 0,
"_valueInteger" : {
"extension" : [
{
"url" : "http://hl7.org/fhir/StructureDefinition/structuredefinition-conformance-derivedFrom",
"valueCanonical" : "http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency"
}
]
}
},
{
"url" : "http://hl7.org/fhir/StructureDefinition/structuredefinition-standards-status",
"valueCode" : "draft",
"_valueCode" : {
"extension" : [
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"url" : "http://hl7.org/fhir/StructureDefinition/structuredefinition-conformance-derivedFrom",
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],
"url" : "http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS",
"version" : "0.1.0",
"name" : "AIdeviceTypeCS",
"title" : "Added Device.type for AI/LLM",
"status" : "active",
"experimental" : false,
"date" : "2025-10-09T23:48:18+00:00",
"publisher" : "HL7 International / Electronic Health Records",
"contact" : [
{
"name" : "HL7 International / Electronic Health Records",
"telecom" : [
{
"system" : "url",
"value" : "http://www.hl7.org/Special/committees/ehr"
},
{
"system" : "email",
"value" : "ehr@lists.hl7.org"
}
]
}
],
"description" : "This CodeSystem contains codes for the Device.type that indicate that the Device is an AI/LLM.",
"jurisdiction" : [
{
"coding" : [
{
"system" : "http://unstats.un.org/unsd/methods/m49/m49.htm",
"code" : "001"
}
]
}
],
"caseSensitive" : true,
"hierarchyMeaning" : "grouped-by",
"content" : "complete",
"count" : 18,
"concept" : [
{
"code" : "AI-By-Scenario",
"display" : "Classification by Application Scenario",
"concept" : [
{
"code" : "Intelligent-Diagnosis-and-Treatment",
"display" : "Intelligent Diagnosis and Treatment",
"definition" : "By analyzing massive volumes of medical data, these AI systems assist doctors in making more accurate diagnostic and treatment decisions."
},
{
"code" : "Medical-Image-Analysis",
"display" : "Medical Image Analysis",
"definition" : "Leveraging deep learning technologies, these AI tools automatically identify lesion areas in medical images."
},
{
"code" : "Personalized-Treatment",
"display" : "Personalized Treatment",
"definition" : "These AI systems create precise patient profiles to formulate personalized treatment plans."
},
{
"code" : "Drug-Discovery-and-Development",
"display" : "Drug Discovery and Development",
"definition" : "AI in this category accelerates the screening of candidate drugs and optimizes the design of clinical trials."
},
{
"code" : "Medical-Quality-Control",
"display" : "Medical Quality Control",
"definition" : "These AI tools are used to generate standardized medical document templates and detect defects in medical documents and images."
},
{
"code" : "Patient-Services",
"display" : "Patient Services",
"definition" : "AI systems here provide patients with services such as intelligent medical guidance, symptom self-assessment, and medical consultation."
}
]
},
{
"code" : "AI-By-DataType",
"display" : "Classification by Processed Data Type",
"concept" : [
{
"code" : "AI-for-Medical-Imaging-Data",
"display" : "AI for Medical Imaging Data",
"definition" : "It mainly processes medical imaging data such as X-rays, MRIs, and CT scans."
},
{
"code" : "AI-for-Physiological-Signal-Data",
"display" : "AI for Physiological Signal Data",
"definition" : "This type of AI deals with physiological signal data like electrocardiograms (ECG) and electroencephalograms (EEG)."
},
{
"code" : "AI-for-Medical-Text-Data",
"display" : "AI for Medical Text Data",
"definition" : "It processes text data such as electronic health records (EHRs) and medical abstracts."
}
]
},
{
"code" : "AI-By-Model",
"display" : "Classification by Technical Model",
"concept" : [
{
"code" : "Machine-Learning-Models",
"display" : "Machine Learning Models",
"definition" : "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."
},
{
"code" : "Deep-Learning-Models",
"display" : "Deep Learning Models",
"definition" : "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."
},
{
"code" : "Large-Language-Models",
"display" : "Large Language Models",
"definition" : "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."
},
{
"code" : "Hybrid-Models",
"display" : "Hybrid Models",
"definition" : "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."
},
{
"code" : "Other-AI-Models",
"display" : "Other AI Models",
"definition" : "This category includes other AI models not covered above, such as graph neural networks (GNNs) for modeling complex relationships in medical data, and evolutionary algorithms for optimizing treatment plans."
}
]
},
{
"code" : "Artificial-Intelligence",
"display" : "All kinds of Artificial Intelligence"
}
]
}
IG © 2024+ HL7 International / Electronic Health Records. Package hl7.fhir.uv.aitransparency#0.1.0 based on FHIR 4.0.1. Generated 2025-10-09
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