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Source1

{
  "resourceType": "CodeSystem",
  "id": "AIdeviceTypeCS",
  "text": {
    "status": "generated",
    "div": "<!-- snip (see above) -->"
  },
  "extension": [
    {
      "url": "http://hl7.org/fhir/StructureDefinition/structuredefinition-wg",
      "valueCode": "ehr"
    },
    {
      "url": "http://hl7.org/fhir/StructureDefinition/structuredefinition-fmm",
      "valueInteger": 2,
      "_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": "trial-use",
      "_valueCode": {
        "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/uv/aitransparency/CodeSystem/AIdeviceTypeCS",
  "version": "1.0.0-ballot",
  "name": "AIdeviceTypeCS",
  "title": "Added Device.type for AI/LLM",
  "status": "active",
  "experimental": false,
  "date": "2025-12-09T17:45:04+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. The codes here were created by AI.",
  "jurisdiction": [
    {
      "coding": [
        {
          "system": "http://unstats.un.org/unsd/methods/m49/m49.htm",
          "code": "001"
        }
      ]
    }
  ],
  "caseSensitive": true,
  "hierarchyMeaning": "grouped-by",
  "content": "complete",
  "count": 17,
  "property": [
    {
      "code": "abstract",
      "uri": "http://hl7.org/fhir/concept-properties#notSelectable",
      "type": "boolean"
    }
  ],
  "concept": [
    {
      "code": "AI-By-Scenario",
      "display": "Classification by Application Scenario",
      "definition": "This category classifies AI systems based on their application scenarios in the medical field.",
      "property": [
        {
          "code": "abstract",
          "valueBoolean": true
        }
      ],
      "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",
      "definition": "This category classifies AI systems based on the types of medical data they primarily process.",
      "property": [
        {
          "code": "abstract",
          "valueBoolean": true
        }
      ],
      "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",
      "definition": "This category classifies AI systems based on the underlying technical models they employ.",
      "property": [
        {
          "code": "abstract",
          "valueBoolean": true
        }
      ],
      "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": "Artificial-Intelligence",
      "display": "All kinds of Artificial Intelligence",
      "definition": "Any type of Artificial Intelligence system, undifferentiated."
    }
  ]
}