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 |
<CodeSystem xmlns="http://hl7.org/fhir">
<id value="AIdeviceTypeCS"/>
<text>
<status value="generated"/>
<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"> 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></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></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></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></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></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></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"> 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></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></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></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"> 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></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></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></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></tr><tr><td>2</td><td style="white-space:nowrap"> Other-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>
</text>
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value="http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency"/>
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<extension
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value="http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency"/>
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<url
value="http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS"/>
<version value="0.1.0"/>
<name value="AIdeviceTypeCS"/>
<title value="Added Device.type for AI/LLM"/>
<status value="active"/>
<experimental value="false"/>
<date value="2025-10-09T23:48:18+00:00"/>
<publisher value="HL7 International / Electronic Health Records"/>
<contact>
<name value="HL7 International / Electronic Health Records"/>
<telecom>
<system value="url"/>
<value value="http://www.hl7.org/Special/committees/ehr"/>
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<telecom>
<system value="email"/>
<value value="ehr@lists.hl7.org"/>
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<description
value="This CodeSystem contains codes for the Device.type that indicate that the Device is an AI/LLM."/>
<jurisdiction>
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<system value="http://unstats.un.org/unsd/methods/m49/m49.htm"/>
<code value="001"/>
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<caseSensitive value="true"/>
<hierarchyMeaning value="grouped-by"/>
<content value="complete"/>
<count value="18"/>
<concept>
<code value="AI-By-Scenario"/>
<display value="Classification by Application Scenario"/>
<concept>
<code value="Intelligent-Diagnosis-and-Treatment"/>
<display value="Intelligent Diagnosis and Treatment"/>
<definition
value="By analyzing massive volumes of medical data, these AI systems assist doctors in making more accurate diagnostic and treatment decisions."/>
</concept>
<concept>
<code value="Medical-Image-Analysis"/>
<display value="Medical Image Analysis"/>
<definition
value="Leveraging deep learning technologies, these AI tools automatically identify lesion areas in medical images."/>
</concept>
<concept>
<code value="Personalized-Treatment"/>
<display value="Personalized Treatment"/>
<definition
value="These AI systems create precise patient profiles to formulate personalized treatment plans."/>
</concept>
<concept>
<code value="Drug-Discovery-and-Development"/>
<display value="Drug Discovery and Development"/>
<definition
value="AI in this category accelerates the screening of candidate drugs and optimizes the design of clinical trials."/>
</concept>
<concept>
<code value="Medical-Quality-Control"/>
<display value="Medical Quality Control"/>
<definition
value="These AI tools are used to generate standardized medical document templates and detect defects in medical documents and images."/>
</concept>
<concept>
<code value="Patient-Services"/>
<display value="Patient Services"/>
<definition
value="AI systems here provide patients with services such as intelligent medical guidance, symptom self-assessment, and medical consultation."/>
</concept>
</concept>
<concept>
<code value="AI-By-DataType"/>
<display value="Classification by Processed Data Type"/>
<concept>
<code value="AI-for-Medical-Imaging-Data"/>
<display value="AI for Medical Imaging Data"/>
<definition
value="It mainly processes medical imaging data such as X-rays, MRIs, and CT scans."/>
</concept>
<concept>
<code value="AI-for-Physiological-Signal-Data"/>
<display value="AI for Physiological Signal Data"/>
<definition
value="This type of AI deals with physiological signal data like electrocardiograms (ECG) and electroencephalograms (EEG)."/>
</concept>
<concept>
<code value="AI-for-Medical-Text-Data"/>
<display value="AI for Medical Text Data"/>
<definition
value="It processes text data such as electronic health records (EHRs) and medical abstracts."/>
</concept>
</concept>
<concept>
<code value="AI-By-Model"/>
<display value="Classification by Technical Model"/>
<concept>
<code value="Machine-Learning-Models"/>
<display value="Machine Learning Models"/>
<definition
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."/>
</concept>
<concept>
<code value="Deep-Learning-Models"/>
<display value="Deep Learning Models"/>
<definition
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."/>
</concept>
<concept>
<code value="Large-Language-Models"/>
<display value="Large Language Models"/>
<definition
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."/>
</concept>
<concept>
<code value="Hybrid-Models"/>
<display value="Hybrid Models"/>
<definition
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."/>
</concept>
<concept>
<code value="Other-AI-Models"/>
<display value="Other AI Models"/>
<definition
value="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."/>
</concept>
</concept>
<concept>
<code value="Artificial-Intelligence"/>
<display value="All kinds of Artificial Intelligence"/>
</concept>
</CodeSystem>
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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