AI Transparency on FHIR
0.1.0 - ci-build 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 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

CodeSystem: Added Device.type for AI/LLM

Official URL: http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS Version: 0.1.0
Standards status: Draft Maturity Level: 0 Computable Name: AIdeviceTypeCS

This CodeSystem contains codes for the Device.type that indicate that the Device is an AI/LLM.

This Code system is referenced in the content logical definition of the following value sets:

This case-sensitive code system http://hl7.org/fhir/uv/aitransparency/CodeSystem/AIdeviceTypeCS defines the following codes in a Grouped By hierarchy:

LvlCodeDisplayDefinition
1 AI-By-Scenario Classification by Application Scenario
2   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.
2   Medical-Image-Analysis Medical Image Analysis Leveraging deep learning technologies, these AI tools automatically identify lesion areas in medical images.
2   Personalized-Treatment Personalized Treatment These AI systems create precise patient profiles to formulate personalized treatment plans.
2   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.
2   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.
2   Patient-Services Patient Services AI systems here provide patients with services such as intelligent medical guidance, symptom self-assessment, and medical consultation.
1 AI-By-DataType Classification by Processed Data Type
2   AI-for-Medical-Imaging-Data AI for Medical Imaging Data It mainly processes medical imaging data such as X-rays, MRIs, and CT scans.
2   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).
2   AI-for-Medical-Text-Data AI for Medical Text Data It processes text data such as electronic health records (EHRs) and medical abstracts.
1 AI-By-Model Classification by Technical Model
2   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.
2   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.
2   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.
2   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   Other-AI-Models Other AI Models 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.
1 Artificial-Intelligence All kinds of Artificial Intelligence