| 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. | |