AIdeviceTypeCS
type: CodeSystem
node role: treeRoot
resource.id:
  value: AIdeviceTypeCS
resource.meta:
  meta.security:
    index: -1
    coding.system:
      value: http://terminology.hl7.org/CodeSystem/v3-ObservationValue
    coding.code:
      value: AIAST
    coding.display:
      value: Artificial Intelligence asserted
resource.language:
  value: en
domain resource.text:
  narrative.status:
    value: generated
  narrative.div: Generated Narrative: CodeSystem AIdeviceTypeCS Security Label: Artificial Intelligence asserted (Details: ObservationValue code AIAST = 'Artificial Intelligence asserted') Properties This code system defines the following properties for its concepts Name Code URI Type Not Selectable abstract http://hl7.org/fhir/concept-properties#notSelectable boolean Concepts This case-sensitive code system http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS defines the following codes in a Grouped By hierarchy: Lvl Code Display Definition Not Selectable 1 Artificial-Intelligence All kinds of Artificial Intelligence Any type of Artificial Intelligence system, undifferentiated. 2 AI-By-Model Classification by Technical Model This category classifies AI systems based on the underlying technical models they employ. true 3 Rule-Based-Systems Rule-Based Systems Systems that apply explicitly authored rules, logic, knowledge representations, or inference procedures to input data. 3 Machine-Learning-Models Machine Learning Models 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. 4 Deep-Learning-Models Deep Learning Models 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. 5 Foundation-Models Foundation …
domain resource.extension:
  index: -1
  extension.url:
    value: http://hl7.org/fhir/StructureDefinition/structuredefinition-wg
  extension.value code:
    value: ehr
domain resource.extension:
  index: -1
  extension.url:
    value: http://hl7.org/fhir/StructureDefinition/structuredefinition-fmm
  extension.value integer:
    value: 2
    element.extension:
      index: -1
      extension.url:
        value: http://hl7.org/fhir/StructureDefinition/structuredefinition-conformance-derivedFrom
      extension.value canonical:
        value: http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency
        link: http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency
domain resource.extension:
  index: -1
  extension.url:
    value: http://hl7.org/fhir/StructureDefinition/structuredefinition-standards-status
  extension.value code:
    value: trial-use
    element.extension:
      index: -1
      extension.url:
        value: http://hl7.org/fhir/StructureDefinition/structuredefinition-conformance-derivedFrom
      extension.value canonical:
        value: http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency
        link: http://hl7.org/fhir/uv/aitransparency/ImplementationGuide/hl7.fhir.uv.aitransparency
code system.url:
  value: http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS
code system.identifier:
  index: -1
  identifier.system:
    value: urn:ietf:rfc:3986
  identifier.value:
    value: urn:oid:2.16.840.1.113883.4.642.40.79.16.1
code system.version:
  value: 1.0.0-current
code system.name:
  value: AIdeviceTypeCS
code system.title:
  value: Device type for Artificial Intelligence
code system.status:
  value: active
code system.experimental:
  value: false
code system.date:
  value: 2026-08-06T14:31:47+00:00
code system.publisher:
  value: HL7 International / Electronic Health Records
code system.contact:
  index: -1
  contact detail.name:
    value: HL7 International / Electronic Health Records
  contact detail.telecom:
    index: -1
    contact point.system:
      value: url
    contact point.value:
      value: http://www.hl7.org/Special/committees/ehr
  contact detail.telecom:
    index: -1
    contact point.system:
      value: email
code system.description:
  value: This CodeSystem contains codes for the Device.type that indicate that the Device is an AI. The codes here were created by AI.
code system.jurisdiction:
  index: -1
  codeable concept.coding:
    index: -1
    coding.system:
      value: http://unstats.un.org/unsd/methods/m49/m49.htm
    coding.code:
      value: 001
code system.case sensitive:
  value: true
code system.hierarchy meaning:
  value: grouped-by
code system.content:
  value: complete
code system.count:
  value: 32
code system.property:
  index: -1
  code system.property.code:
    value: abstract
  code system.property.uri:
    value: http://hl7.org/fhir/concept-properties#notSelectable
  code system.property.type:
    value: boolean
code system.concept:
  index: -1
  code system.concept.code:
    value: Artificial-Intelligence
  code system.concept.display:
    value: All kinds of Artificial Intelligence
  code system.concept.definition:
    value: Any type of Artificial Intelligence system, undifferentiated.
  code system.concept.concept:
    index: -1
    code system.concept.code:
      value: AI-By-Model
    code system.concept.display:
      value: Classification by Technical Model
    code system.concept.definition:
      value: This category classifies AI systems based on the underlying technical models they employ.
    code system.concept.property:
      index: -1
      code system.concept.property.code:
        value: abstract
      code system.concept.property.value boolean:
        value: true
    code system.concept.concept:
      index: -1
      code system.concept.code:
        value: Rule-Based-Systems
      code system.concept.display:
        value: Rule-Based Systems
      code system.concept.definition:
        value: Systems that apply explicitly authored rules, logic, knowledge representations, or inference procedures to input data.
    code system.concept.concept:
      index: -1
      code system.concept.code:
        value: Machine-Learning-Models
      code system.concept.display:
        value: Machine Learning Models
      code system.concept.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.
      code system.concept.concept:
        index: -1
    code system.concept.concept:
      index: -1
      code system.concept.code:
        value: Hybrid-Models
      code system.concept.display:
        value: Hybrid Models
      code system.concept.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.
  code system.concept.concept:
    index: -1
    code system.concept.code:
      value: AI-By-Behavior
    code system.concept.display:
      value: Classification by Output Behavior
    code system.concept.definition:
      value: Groups AI systems by whether identical inputs and execution conditions are expected to produce identical outputs.
    code system.concept.property:
      index: -1
      code system.concept.property.code:
        value: abstract
      code system.concept.property.value boolean:
        value: true
    code system.concept.concept:
      index: -1
      code system.concept.code:
        value: Deterministic-AI
      code system.concept.display:
        value: Deterministic AI
      code system.concept.definition:
        value: An AI system that produces the same output for the same input when its model, configuration, state, and execution environment are unchanged.
    code system.concept.concept:
      index: -1
      code system.concept.code:
        value: Non-Deterministic-AI
      code system.concept.display:
        value: Non-deterministic AI
      code system.concept.definition:
        value: An AI system whose output may vary for the same input and execution conditions, for example because it uses probabilistic sampling or stochastic processing.
  code system.concept.concept:
    index: -1
    code system.concept.code:
      value: AI-By-Scenario
    code system.concept.display:
      value: Classification by Application Scenario
    code system.concept.definition:
      value: This category classifies AI systems based on their application scenarios in the medical field.
    code system.concept.property:
      index: -1
      code system.concept.property.code:
        value: abstract

value: Deep-Learning-Models

value: Deep Learning Models

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.

index: -1
code system.concept.code:
  value: Foundation-Models
code system.concept.display:
  value: Foundation Models
code system.concept.definition:
  value: Deep learning models trained on broad data at scale that can be adapted to a range of downstream tasks.
code system.concept.property:
  index: -1
  code system.concept.property.code:
    value: abstract
  code system.concept.property.value boolean:
    value: true
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: Large-Language-Models
  code system.concept.display:
    value: Large Language Models
  code system.concept.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.
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: Vision-Foundation-Models
  code system.concept.display:
    value: Vision Foundation Models
  code system.concept.definition:
    value: Foundation models trained primarily to process and represent visual data for adaptation to vision tasks.
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: Vision-Language-Models
  code system.concept.display:
    value: Vision-Language Models
  code system.concept.definition:
    value: Foundation models that jointly process visual and language data.

value: true

index: -1
code system.concept.code:
  value: Intelligent-Diagnosis-and-Treatment
code system.concept.display:
  value: Intelligent Diagnosis and Treatment
code system.concept.definition:
  value: By analyzing massive volumes of medical data, these AI systems assist doctors in making more accurate diagnostic and treatment decisions.

index: -1
code system.concept.code:
  value: Medical-Image-Analysis
code system.concept.display:
  value: Medical Image Analysis
code system.concept.definition:
  value: Leveraging deep learning technologies, these AI tools automatically identify lesion areas in medical images.

index: -1
code system.concept.code:
  value: Personalized-Treatment
code system.concept.display:
  value: Personalized Treatment
code system.concept.definition:
  value: These AI systems create precise patient profiles to formulate personalized treatment plans.

index: -1
code system.concept.code:
  value: Clinical-Monitoring-and-Early-Warning
code system.concept.display:
  value: Clinical Monitoring and Early Warning
code system.concept.definition:
  value: Monitors longitudinal or real-time patient data to identify deterioration, adverse events, or other conditions requiring attention.

index: -1
code system.concept.code:
  value: Clinical-Documentation-and-Workflow
code system.concept.display:
  value: Clinical Documentation and Workflow
code system.concept.definition:
  value: Creates, summarizes, extracts, codes, routes, or quality-checks clinical and administrative information to support healthcare workflows.

index: -1
code system.concept.code:
  value: Drug-Discovery-and-Development
code system.concept.display:
  value: Drug Discovery and Development
code system.concept.definition:
  value: AI in this category accelerates the screening of candidate drugs and optimizes the design of clinical trials.

index: -1
code system.concept.code:
  value: Medical-Quality-Control
code system.concept.display:
  value: Medical Quality Control
code system.concept.definition:
  value: These AI tools are used to generate standardized medical document templates and detect defects in medical documents and images.

index: -1
code system.concept.code:
  value: Patient-Services
code system.concept.display:
  value: Patient Services
code system.concept.definition:
  value: AI systems here provide patients with services such as intelligent medical guidance, symptom self-assessment, and medical consultation.

index: -1
code system.concept.code:
  value: Population-Health-and-Operations
code system.concept.display:
  value: Population Health and Healthcare Operations
code system.concept.definition:
  value: Supports population stratification, public health, resource planning, scheduling, logistics, or other healthcare operational activities.

index: -1
code system.concept.code:
  value: AI-By-DataType
code system.concept.display:
  value: Classification by Processed Data Type
code system.concept.definition:
  value: This category classifies AI systems based on the types of medical data they primarily process.
code system.concept.property:
  index: -1
  code system.concept.property.code:
    value: abstract
  code system.concept.property.value boolean:
    value: true
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: AI-for-Medical-Imaging-Data
  code system.concept.display:
    value: AI for Medical Imaging Data
  code system.concept.definition:
    value: It mainly processes medical imaging data such as X-rays, MRIs, and CT scans.
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: AI-for-Physiological-Signal-Data
  code system.concept.display:
    value: AI for Physiological Signal Data
  code system.concept.definition:
    value: This type of AI deals with physiological signal data like electrocardiograms (ECG) and electroencephalograms (EEG).
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: AI-for-Medical-Text-Data
  code system.concept.display:
    value: AI for Medical Text Data
  code system.concept.definition:
    value: It processes text data such as electronic health records (EHRs) and medical abstracts.
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: AI-for-Structured-Clinical-Data
  code system.concept.display:
    value: Structured Clinical Data
  code system.concept.definition:
    value: Processes coded, tabular, or relational health data such as diagnoses, medications, laboratory results, observations, claims, or FHIR resources.
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: AI-for-Genomic-and-Omics-Data
  code system.concept.display:
    value: Genomic and Omics Data
  code system.concept.definition:
    value: Processes genomic, transcriptomic, proteomic, metabolomic, or related molecular data.
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: AI-for-Audio-Data
  code system.concept.display:
    value: Audio Data
  code system.concept.definition:
    value: Processes speech, heart sounds, respiratory sounds, or other clinically relevant audio.
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: AI-for-Video-Data
  code system.concept.display:
    value: Video Data
  code system.concept.definition:
    value: Processes temporal image sequences such as endoscopy, ultrasound cine loops, gait recordings, or procedure video.
code system.concept.concept:
  index: -1
  code system.concept.code:
    value: AI-for-Multimodal-Data
  code system.concept.display:
    value: Multimodal Data
  code system.concept.definition:
    value: Jointly processes two or more data modalities, such as images and text or physiological signals and structured clinical data.