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 Models Deep learning models trained on broad data at scale that can be adapted to a range of downstream tasks. true 6 Large-Language-Models Large Language Models 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. 6 Vision-Foundation-Models Vision Foundation Models Foundation models trained primarily to process and represent visual data for adaptation to vision tasks. 6 Vision-Language-Models Vision-Language Models Foundation models that jointly process visual and language data. 3 Hybrid-Models Hybrid Models 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. 2 AI-By-Behavior Classification by Output Behavior Groups AI systems by whether identical inputs and execution conditions are expected to produce identical outputs. true 3 Deterministic-AI Deterministic AI An AI system that produces the same output for the same input when its model, configuration, state, and execution environment are unchanged. 3 Non-Deterministic-AI Non-deterministic AI An AI system whose output may vary for the same input and execution conditions, for example because it uses probabilistic sampling or stochastic processing. 2 AI-By-Scenario Classification by Application Scenario This category classifies AI systems based on their application scenarios in the medical field. true 3 Intelligent-Diagnosis-and-Treatment Intelligent Diagnosis and Treatment By analyzing massive volumes of medical data, these AI systems assist doctors in making more accurate diagnostic and treatment decisions. 3 Medical-Image-Analysis Medical Image Analysis Leveraging deep learning technologies, these AI tools automatically identify lesion areas in medical images. 3 Personalized-Treatment Personalized Treatment These AI systems create precise patient profiles to formulate personalized treatment plans. 3 Clinical-Monitoring-and-Early-Warning Clinical Monitoring and Early Warning Monitors longitudinal or real-time patient data to identify deterioration, adverse events, or other conditions requiring attention. 3 Clinical-Documentation-and-Workflow Clinical Documentation and Workflow Creates, summarizes, extracts, codes, routes, or quality-checks clinical and administrative information to support healthcare workflows. 3 Drug-Discovery-and-Development Drug Discovery and Development AI in this category accelerates the screening of candidate drugs and optimizes the design of clinical trials. 3 Medical-Quality-Control Medical Quality Control These AI tools are used to generate standardized medical document templates and detect defects in medical documents and images. 3 Patient-Services Patient Services AI systems here provide patients with services such as intelligent medical guidance, symptom self-assessment, and medical consultation. 3 Population-Health-and-Operations Population Health and Healthcare Operations Supports population stratification, public health, resource planning, scheduling, logistics, or other healthcare operational activities. 2 AI-By-DataType Classification by Processed Data Type This category classifies AI systems based on the types of medical data they primarily process. true 3 AI-for-Medical-Imaging-Data AI for Medical Imaging Data It mainly processes medical imaging data such as X-rays, MRIs, and CT scans. 3 AI-for-Physiological-Signal-Data AI for Physiological Signal Data This type of AI deals with physiological signal data like electrocardiograms (ECG) and electroencephalograms (EEG). 3 AI-for-Medical-Text-Data AI for Medical Text Data It processes text data such as electronic health records (EHRs) and medical abstracts. 3 AI-for-Structured-Clinical-Data Structured Clinical Data Processes coded, tabular, or relational health data such as diagnoses, medications, laboratory results, observations, claims, or FHIR resources. 3 AI-for-Genomic-and-Omics-Data Genomic and Omics Data Processes genomic, transcriptomic, proteomic, metabolomic, or related molecular data. 3 AI-for-Audio-Data Audio Data Processes speech, heart sounds, respiratory sounds, or other clinically relevant audio. 3 AI-for-Video-Data Video Data Processes temporal image sequences such as endoscopy, ultrasound cine loops, gait recordings, or procedure video. 3 AI-for-Multimodal-Data Multimodal Data Jointly processes two or more data modalities, such as images and text or physiological signals and structured clinical data.
HL7 International / Electronic Health Records
This CodeSystem contains codes for the Device.type that indicate that the Device is an AI. The codes here were created by AI.
Any type of Artificial Intelligence system, undifferentiated.
This category classifies AI systems based on the underlying technical models they employ.
Systems that apply explicitly authored rules, logic, knowledge representations, or inference procedures to input data.
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.
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.
Deep learning models trained on broad data at scale that can be adapted to a range of downstream tasks.
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.
Foundation models trained primarily to process and represent visual data for adaptation to vision tasks.
Foundation models that jointly process visual and language data.
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.
Groups AI systems by whether identical inputs and execution conditions are expected to produce identical outputs.
An AI system that produces the same output for the same input when its model, configuration, state, and execution environment are unchanged.
An AI system whose output may vary for the same input and execution conditions, for example because it uses probabilistic sampling or stochastic processing.
This category classifies AI systems based on their application scenarios in the medical field.
By analyzing massive volumes of medical data, these AI systems assist doctors in making more accurate diagnostic and treatment decisions.
Leveraging deep learning technologies, these AI tools automatically identify lesion areas in medical images.
These AI systems create precise patient profiles to formulate personalized treatment plans.
Monitors longitudinal or real-time patient data to identify deterioration, adverse events, or other conditions requiring attention.
Creates, summarizes, extracts, codes, routes, or quality-checks clinical and administrative information to support healthcare workflows.
AI in this category accelerates the screening of candidate drugs and optimizes the design of clinical trials.
These AI tools are used to generate standardized medical document templates and detect defects in medical documents and images.
AI systems here provide patients with services such as intelligent medical guidance, symptom self-assessment, and medical consultation.
Population Health and Healthcare Operations
Supports population stratification, public health, resource planning, scheduling, logistics, or other healthcare operational activities.
This category classifies AI systems based on the types of medical data they primarily process.
It mainly processes medical imaging data such as X-rays, MRIs, and CT scans.
This type of AI deals with physiological signal data like electrocardiograms (ECG) and electroencephalograms (EEG).
It processes text data such as electronic health records (EHRs) and medical abstracts.
Processes coded, tabular, or relational health data such as diagnoses, medications, laboratory results, observations, claims, or FHIR resources.
Processes genomic, transcriptomic, proteomic, metabolomic, or related molecular data.
Processes speech, heart sounds, respiratory sounds, or other clinically relevant audio.
Processes temporal image sequences such as endoscopy, ultrasound cine loops, gait recordings, or procedure video.
Jointly processes two or more data modalities, such as images and text or physiological signals and structured clinical data.