AI Transparency on FHIR
1.0.0-ballot - STU1 Ballot International flag

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-ballot 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

ValueSet: Recommended Device type codes for Artificial Intelligence

Official URL: http://hl7.org/fhir/uv/aitransparency/ValueSet/AIdeviceTypeVS Version: 1.0.0-ballot
Standards status: Trial-use Active as of 2026-04-21 Maturity Level: 2 Computable Name: AIdeviceTypeVS

Subset from HL7, plus those defined here

References

Logical Definition (CLD)

 

Expansion

Expansion performed internally based on codesystem Device type for Artificial Intelligence v1.0.0-ballot (CodeSystem)

This value set contains 17 concepts

LevelSystemCodeDisplay (en)DefinitionJSONXML
1http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS  AI-By-ScenarioClassification by Application ScenarioThis category classifies AI systems based on their application scenarios in the medical field.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    Intelligent-Diagnosis-and-TreatmentIntelligent Diagnosis and TreatmentBy analyzing massive volumes of medical data, these AI systems assist doctors in making more accurate diagnostic and treatment decisions.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    Medical-Image-AnalysisMedical Image AnalysisLeveraging deep learning technologies, these AI tools automatically identify lesion areas in medical images.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    Personalized-TreatmentPersonalized TreatmentThese AI systems create precise patient profiles to formulate personalized treatment plans.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    Drug-Discovery-and-DevelopmentDrug Discovery and DevelopmentAI in this category accelerates the screening of candidate drugs and optimizes the design of clinical trials.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    Medical-Quality-ControlMedical Quality ControlThese AI tools are used to generate standardized medical document templates and detect defects in medical documents and images.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    Patient-ServicesPatient ServicesAI systems here provide patients with services such as intelligent medical guidance, symptom self-assessment, and medical consultation.
1http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS  AI-By-DataTypeClassification by Processed Data TypeThis category classifies AI systems based on the types of medical data they primarily process.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    AI-for-Medical-Imaging-DataAI for Medical Imaging DataIt mainly processes medical imaging data such as X-rays, MRIs, and CT scans.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    AI-for-Physiological-Signal-DataAI for Physiological Signal DataThis type of AI deals with physiological signal data like electrocardiograms (ECG) and electroencephalograms (EEG).
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    AI-for-Medical-Text-DataAI for Medical Text DataIt processes text data such as electronic health records (EHRs) and medical abstracts.
1http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS  AI-By-ModelClassification by Technical ModelThis category classifies AI systems based on the underlying technical models they employ.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    Machine-Learning-ModelsMachine Learning ModelsThey 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.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    Deep-Learning-ModelsDeep Learning ModelsExamples 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.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    Large-Language-ModelsLarge Language ModelsThese 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.
2http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS    Hybrid-ModelsHybrid ModelsThese 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.
1http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS  Artificial-IntelligenceAll kinds of Artificial IntelligenceAny type of Artificial Intelligence system, undifferentiated.

Description of the above table(s).