ScanLab Integration Specifications

1. Introduction

The successful deployment of AI-assisted diagnostic systems in medical imaging requires more than algorithm accuracy—it demands seamless integration with existing clinical infrastructure. As established throughout this research series, the gap between promising AI research results and sustainable clinical deployment often derives not from algorithmic limitations but from integration failures: systems that cannot communicate with existing PACS, workflows that disrupt rather than enhance radiologist productivity, and architectures that cannot scale to meet institutional demands.

This specification document addresses these challenges by providing a comprehensive technical blueprint for AI integration at ScanLab and similar Ukrainian diagnostic imaging facilities. The specifications are grounded in international standards—primarily DICOM, HL7 FHIR, and IHE integration profiles—while explicitly accommodating Ukrainian operational realities including infrastructure variability, regulatory requirements, and wartime operational continuity needs.

1.1 Scope and Objectives

This document specifies:

1.2 Standards Foundation

This specification mandates adherence to established healthcare interoperability standards, ensuring vendor-neutral integration and future extensibility:

2. System Architecture

The ScanLab AI integration architecture follows a modular, microservices-based design that enables independent scaling of components, supports both on-premises and hybrid cloud deployment, and maintains operational continuity during partial system failures.

2.1 High-Level Architecture Overview

flowchart TB
    subgraph External["External Systems"]
        MOD[Imaging Modalities
CT/MRI/X-ray/US]
        RIS[Radiology Information
System]
        EHR[Electronic Health
Record System]
    end
    
    subgraph Gateway["Integration Gateway Layer"]
        DICOMr[DICOM Router/
Orchestrator]
        HL7E[HL7 FHIR
Engine]
        MWL[Modality
Worklist]
    end
    
    subgraph Core["AI Processing Core"]
        QUEUE[Task Queue
Manager]
        INF1[Inference Node 1
GPU Accelerated]
        INF2[Inference Node 2
GPU Accelerated]
        INFN[Inference Node N
Scalable]
        MODEL[Model
Repository]
    end
    
    subgraph Storage["Data Storage Layer"]
        PACS[(PACS/VNA
Archive)]
        FHIR[(FHIR
Repository)]
        AUDIT[(Audit Log
Database)]
    end
    
    subgraph Presentation["Results Presentation"]
        VIEWER[PACS
Workstation]
        REPORT[Structured
Report Generator]
        DASH[Monitoring
Dashboard]
    end
    
    MOD -->|DICOM| DICOMr
    RIS -->|HL7 v2/FHIR| HL7E
    EHR -->|FHIR R4| HL7E
    
    DICOMr -->|WADO-RS| QUEUE
    HL7E -->|Task Context| QUEUE
    MWL |Worklist| DICOMr
    
    QUEUE --> INF1
    QUEUE --> INF2
    QUEUE --> INFN
    MODEL --> INF1
    MODEL --> INF2
    MODEL --> INFN
    
    INF1 -->|STOW-RS| PACS
    INF2 -->|STOW-RS| PACS
    INFN -->|STOW-RS| PACS
    
    INF1 -->|DICOM SR| REPORT
    DICOMr -->|C-STORE| PACS
    HL7E --> FHIR
    
    PACS --> VIEWER
    REPORT --> VIEWER
    QUEUE --> AUDIT
    INF1 --> AUDIT
    AUDIT --> DASH

2.2 Component Specifications

2.2.1 Integration Gateway Layer

The Integration Gateway serves as the primary interface between existing clinical systems and the AI processing infrastructure. It implements protocol translation, message routing, and workflow orchestration.

2.2.2 AI Processing Core

The processing core executes AI inference pipelines on medical images. The architecture supports horizontal scaling through containerized inference nodes managed by Kubernetes orchestration.

2.2.3 Model Repository

The Model Repository maintains versioned AI models with metadata, validation certificates, and deployment configurations:

model-repository/
├── chest-xray-detection/
│   ├── 1/                          # Version 1
│   │   ├── model.onnx
│   │   ├── config.pbtxt
│   │   └── metadata.json
│   ├── 2/                          # Version 2 (current)
│   │   ├── model.plan              # TensorRT optimized
│   │   ├── config.pbtxt
│   │   ├── metadata.json
│   │   └── validation/
│   │       ├── performance_report.json
│   │       └── regulatory_clearance.pdf
│   └── model_info.json
├── ct-lung-nodule/
│   └── ...
└── mri-brain-segmentation/
    └── ...

2.3 Deployment Models

ScanLab integration supports three deployment configurations to accommodate varying infrastructure capabilities:

flowchart LR
    subgraph OnPrem["On-Premises Deployment"]
        direction TB
        OP_GW[Gateway]
        OP_INF[Inference
Cluster]
        OP_PACS[(PACS)]
        OP_GW --> OP_INF
        OP_INF --> OP_PACS
    end
    
    subgraph Hybrid["Hybrid Cloud Deployment"]
        direction TB
        HY_GW[Gateway]
        HY_LOCAL[Local
Inference]
        HY_CLOUD[Cloud
Inference]
        HY_PACS[(PACS)]
        HY_GW --> HY_LOCAL
        HY_GW -->|VPN/TLS| HY_CLOUD
        HY_LOCAL --> HY_PACS
        HY_CLOUD -->|Results| HY_GW
    end
    
    subgraph Cloud["Cloud-Native Deployment"]
        direction TB
        CL_GW[Edge
Gateway]
        CL_PROC[Cloud
Processing]
        CL_STORE[(Cloud
Archive)]
        CL_GW -->|DICOMweb| CL_PROC
        CL_PROC --> CL_STORE
    end

3. Interface Specifications

3.1 DICOMweb Services

The integration implements DICOMweb RESTful services for standards-based image exchange. All services require TLS 1.3 encryption and OAuth 2.0 bearer token authentication.

3.1.1 WADO-RS (Web Access to DICOM Objects - RESTful)

WADO-RS enables retrieval of DICOM studies, series, and instances for AI processing:

3.1.2 STOW-RS (Store Over the Web - RESTful)

STOW-RS enables AI systems to store results (secondary captures, segmentation masks, structured reports) back to PACS:

# Example STOW-RS Request for AI Segmentation Result
POST /dicomweb/studies HTTP/1.1
Host: pacs.scanlab.ua
Authorization: Bearer <oauth_token>
Content-Type: multipart/related; type="application/dicom"; boundary=myboundary
Accept: application/dicom+json

--myboundary
Content-Type: application/dicom

<DICOM Segmentation Object - Binary>
--myboundary--

3.1.3 QIDO-RS (Query based on ID for DICOM Objects - RESTful)

QIDO-RS enables querying for studies matching specific criteria, essential for worklist management and retrospective analysis:

3.2 HL7 FHIR R4 Resources

FHIR resources provide clinical context for AI processing and enable structured results delivery:

3.2.1 ImagingStudy Resource

{
  "resourceType": "ImagingStudy",
  "id": "scanlab-study-12345",
  "identifier": [{
    "system": "urn:dicom:uid",
    "value": "urn:oid:1.2.804.114.ScanLab.2026.1.12345"
  }],
  "status": "available",
  "subject": {
    "reference": "Patient/ua-patient-67890"
  },
  "started": "2026-02-11T08:30:00+02:00",
  "numberOfSeries": 3,
  "numberOfInstances": 245,
  "modality": [{
    "system": "http://dicom.nema.org/resources/ontology/DCM",
    "code": "CT"
  }],
  "procedureCode": [{
    "coding": [{
      "system": "http://loinc.org",
      "code": "24627-2",
      "display": "CT Chest"
    }]
  }],
  "endpoint": [{
    "reference": "Endpoint/scanlab-dicomweb"
  }],
  "series": [...]
}

3.2.2 Task Resource (IHE AI-WI Profile)

The Task resource implements the IHE AI Workflow for Imaging profile, managing AI processing requests:

{
  "resourceType": "Task",
  "id": "ai-task-98765",
  "status": "in-progress",
  "intent": "order",
  "priority": "urgent",
  "code": {
    "coding": [{
      "system": "http://scanlab.ua/ai-tasks",
      "code": "lung-nodule-detection",
      "display": "AI Lung Nodule Detection"
    }]
  },
  "focus": {
    "reference": "ImagingStudy/scanlab-study-12345"
  },
  "for": {
    "reference": "Patient/ua-patient-67890"
  },
  "authoredOn": "2026-02-11T08:31:00+02:00",
  "lastModified": "2026-02-11T08:31:15+02:00",
  "requester": {
    "reference": "Device/scanlab-dicom-router"
  },
  "owner": {
    "reference": "Device/inference-node-1"
  },
  "input": [{
    "type": {
      "coding": [{
        "system": "http://hl7.org/fhir/uv/imaging-ai/CodeSystem/ai-input-type",
        "code": "imaging-study"
      }]
    },
    "valueReference": {
      "reference": "ImagingStudy/scanlab-study-12345"
    }
  }],
  "output": []
}

3.2.3 DiagnosticReport Resource (AI Results)

AI findings are encoded as DiagnosticReport resources linked to Observation resources for discrete findings:

flowchart LR
    subgraph FHIR["FHIR Resources"]
        DR[DiagnosticReport
AI Analysis Report]
        OBS1[Observation
Nodule Finding 1]
        OBS2[Observation
Nodule Finding 2]
        IS[ImagingStudy
Source Study]
        MEDIA[Media
Annotated Image]
    end
    
    DR -->|result| OBS1
    DR -->|result| OBS2
    DR -->|imagingStudy| IS
    DR -->|media| MEDIA
    OBS1 -->|derivedFrom| IS
    OBS2 -->|derivedFrom| IS

3.3 AI Inference API

The inference API provides endpoints for submitting studies to AI models and retrieving results:

3.3.1 Inference Request

# Request Body
{
  "study_instance_uid": "1.2.804.114.ScanLab.2026.1.12345",
  "model_id": "lung-nodule-detection",
  "model_version": "2",
  "priority": "normal",  // "emergency" | "urgent" | "normal" | "routine"
  "callback_url": "https://pacs.scanlab.ua/ai-results/callback",
  "parameters": {
    "confidence_threshold": 0.7,
    "output_formats": ["dicom_sr", "dicom_seg", "fhir_observation"],
    "include_heatmaps": true
  },
  "clinical_context": {
    "indication": "Follow-up lung nodule surveillance",
    "patient_history": "Previous nodule detected 6 months ago"
  }
}

# Response
{
  "task_id": "ai-task-98765",
  "status": "accepted",
  "estimated_completion_seconds": 25,
  "queue_position": 3,
  "links": {
    "status": "/api/v1/inference/ai-task-98765/status",
    "results": "/api/v1/inference/ai-task-98765/results",
    "cancel": "/api/v1/inference/ai-task-98765/cancel"
  }
}

3.3.2 Inference Status

{
  "task_id": "ai-task-98765",
  "status": "completed",  // "queued" | "preprocessing" | "inferring" | "postprocessing" | "completed" | "failed"
  "progress_percent": 100,
  "started_at": "2026-02-11T08:31:15+02:00",
  "completed_at": "2026-02-11T08:31:38+02:00",
  "inference_time_ms": 2847,
  "total_time_ms": 23124,
  "model_info": {
    "id": "lung-nodule-detection",
    "version": "2",
    "regulatory_status": "CE-marked Class IIa"
  }
}

3.3.3 Inference Results

{
  "task_id": "ai-task-98765",
  "study_instance_uid": "1.2.804.114.ScanLab.2026.1.12345",
  "findings": [
    {
      "finding_id": "f001",
      "type": "pulmonary_nodule",
      "confidence": 0.94,
      "location": {
        "series_instance_uid": "1.2.804.114.ScanLab.2026.1.12345.1",
        "instance_number": 87,
        "coordinates": {"x": 234, "y": 156, "z": 87},
        "anatomical_region": {
          "system": "http://radlex.org",
          "code": "RID1302",
          "display": "Right lower lobe"
        }
      },
      "measurements": {
        "diameter_mm": 8.3,
        "volume_mm3": 302.4
      },
      "characteristics": {
        "texture": "part-solid",
        "margin": "spiculated",
        "lung_rads_category": "4A"
      }
    }
  ],
  "output_references": {
    "dicom_sr": {
      "sop_instance_uid": "1.2.804.114.ScanLab.2026.1.12345.SR.1",
      "retrieve_url": "/dicomweb/studies/.../series/.../instances/..."
    },
    "dicom_segmentation": {
      "sop_instance_uid": "1.2.804.114.ScanLab.2026.1.12345.SEG.1",
      "retrieve_url": "/dicomweb/studies/.../series/.../instances/..."
    },
    "fhir_diagnostic_report": {
      "reference": "DiagnosticReport/ai-report-98765",
      "retrieve_url": "/fhir/DiagnosticReport/ai-report-98765"
    }
  },
  "quality_metrics": {
    "input_quality_score": 0.92,
    "artifacts_detected": false,
    "coverage_complete": true
  }
}

4. Data Flow Specifications

4.1 Study Acquisition and Processing Pipeline

The following sequence diagram illustrates the complete data flow from image acquisition through AI processing to results presentation:

sequenceDiagram
    participant MOD as Modality (CT/MRI)
    participant ROUTER as DICOM Router
    participant QUEUE as Task Queue
    participant INF as Inference Node
    participant MODEL as Model Repository
    participant PACS as PACS Archive
    participant WS as PACS Workstation
    participant AUDIT as Audit Log
    
    Note over MOD,AUDIT: Study Acquisition Phase
    MOD->>ROUTER: C-STORE (DICOM images)
    ROUTER->>PACS: Forward C-STORE
    ROUTER->>AUDIT: Log: Study received
    ROUTER->>ROUTER: Apply routing rules
    
    Note over MOD,AUDIT: AI Task Creation Phase
    ROUTER->>QUEUE: Create AI Task (FHIR Task)
    QUEUE->>QUEUE: Prioritize by urgency
    QUEUE->>AUDIT: Log: Task created
    
    Note over MOD,AUDIT: Inference Phase
    QUEUE->>INF: Assign task
    INF->>PACS: WADO-RS retrieve study
    PACS-->>INF: Return DICOM instances
    INF->>MODEL: Load model (if not cached)
    MODEL-->>INF: Return model weights
    INF->>INF: Preprocess images
    INF->>INF: Run inference
    INF->>INF: Postprocess results
    INF->>AUDIT: Log: Inference complete
    
    Note over MOD,AUDIT: Results Storage Phase
    INF->>PACS: STOW-RS (DICOM SR)
    INF->>PACS: STOW-RS (Segmentation)
    PACS-->>INF: Confirm storage
    INF->>QUEUE: Update task: completed
    
    Note over MOD,AUDIT: Results Presentation Phase
    QUEUE->>WS: Notify: AI results available
    WS->>PACS: Retrieve AI results
    PACS-->>WS: Return SR + Segmentation
    WS->>WS: Display with source images

4.2 Priority-Based Routing

The system implements intelligent routing based on study characteristics and clinical urgency:

flowchart TD
    START[Study Received] --> CHECK{Priority
Assessment}
    
    CHECK -->|ED/STAT/Stroke| P1[Emergency Queue
P1]
    CHECK -->|Inpatient/Oncology| P2[Urgent Queue
P2]
    CHECK -->|Outpatient Standard| P3[Normal Queue
P3]
    CHECK -->|Screening/Batch| P4[Routine Queue
P4]
    
    P1 --> FAST[Fast-Track
Inference Node]
    P2 --> STANDARD[Standard
Inference Pool]
    P3 --> STANDARD
    P4 --> BATCH[Batch Processing
Off-Peak]
    
    FAST -->|||<4h| RESULT

4.3 Results Encoding Specifications

4.3.1 DICOM Structured Report (SR)

AI findings are encoded in DICOM SR using the Comprehensive SR SOP Class with TID 1500 (Measurement Report):

4.3.2 DICOM Segmentation

Anatomical segmentation masks are stored as DICOM Segmentation objects:

5. Security Specifications

5.1 Authentication and Authorization

sequenceDiagram
    participant Client
    participant Gateway as API Gateway
    participant Auth as OAuth 2.0 Server
    participant API as AI Service
    
    Client->>Auth: Request Token (client_credentials)
    Auth-->>Client: Access Token (JWT)
    
    Client->>Gateway: API Request + Bearer Token
    Gateway->>Gateway: Validate JWT signature
    Gateway->>Gateway: Check token claims/scopes
    
    alt Token Valid
        Gateway->>API: Forward request
        API-->>Gateway: Response
        Gateway-->>Client: Return response
    else Token Invalid/Expired
        Gateway-->>Client: 401 Unauthorized
    end

5.2 Data Protection Requirements

5.3 Audit Log Specification

All system events are logged in an immutable audit trail conforming to IHE ATNA (Audit Trail and Node Authentication):

{
  "event_id": "audit-2026-02-11-083115-001",
  "timestamp": "2026-02-11T08:31:15.234+02:00",
  "event_type": "AI_INFERENCE_INITIATED",
  "event_outcome": "SUCCESS",
  "actor": {
    "user_id": "system:dicom-router",
    "user_role": "SYSTEM",
    "network_access_point": "192.168.10.15"
  },
  "patient": {
    "patient_id": "UA-PAT-67890",
    "id_type": "National Health ID"
  },
  "study": {
    "study_instance_uid": "1.2.804.114.ScanLab.2026.1.12345",
    "accession_number": "SL-2026-001234"
  },
  "ai_processing": {
    "model_id": "lung-nodule-detection",
    "model_version": "2",
    "task_id": "ai-task-98765"
  },
  "source_application": {
    "ae_title": "SCANLAB_ROUTER",
    "application_name": "ScanLab DICOM Router v2.1"
  }
}

6. Performance Specifications

6.1 Performance Targets

6.2 Scalability Specifications

The architecture supports horizontal scaling through Kubernetes-based orchestration:

flowchart LR
    subgraph AutoScale["Auto-Scaling Logic"]
        METRICS[Queue Depth
Latency Metrics]
        HPA[Horizontal Pod
Autoscaler]
        SCALE[Scale Decision]
    end
    
    subgraph Nodes["Inference Pool"]
        N1[Node 1
GPU]
        N2[Node 2
GPU]
        N3[Node 3
GPU]
        NN[Node N
Elastic]
    end
    
    METRICS --> HPA
    HPA --> SCALE
    SCALE -->|Scale Up| NN
    SCALE -->|Scale Down| N3

6.3 Failure Handling and Resilience

flowchart TD
    START[Task Submitted] --> ATTEMPT1{Attempt 1}
    ATTEMPT1 -->|Success| DONE[Complete]
    ATTEMPT1 -->|Failure| RETRY1[Wait 5s]
    
    RETRY1 --> ATTEMPT2{Attempt 2
Different Node}
    ATTEMPT2 -->|Success| DONE
    ATTEMPT2 -->|Failure| RETRY2[Wait 15s]
    
    RETRY2 --> ATTEMPT3{Attempt 3
Different Node}
    ATTEMPT3 -->|Success| DONE
    ATTEMPT3 -->|Failure| FAIL[Mark Failed
Alert Operations]
    
    FAIL --> MANUAL[Manual Review
Required]

7. Hardware Specifications

7.1 Recommended Hardware Configurations

7.2 Network Topology

flowchart TB
    subgraph Clinical["Clinical Network (VLAN 10)"]
        MOD1[CT Scanner]
        MOD2[MRI Scanner]
        MOD3[X-Ray]
        PACS[(PACS)]
        WS[Workstations]
    end
    
    subgraph DMZ["DMZ (VLAN 20)"]
        FW1[Firewall]
        LB[Load Balancer]
        GW[Integration Gateway]
    end
    
    subgraph AI["AI Processing Network (VLAN 30)"]
        INF1[Inference Node 1]
        INF2[Inference Node 2]
        STORE[(Model Storage)]
    end
    
    subgraph Mgmt["Management (VLAN 40)"]
        MON[Monitoring]
        LOG[(Audit Logs)]
        ADMIN[Admin Console]
    end
    
    MOD1 --> PACS
    MOD2 --> PACS
    MOD3 --> PACS
    PACS  FW1
    WS  FW1
    
    FW1  LB
    LB  GW
    
    GW  INF1
    GW  INF2
    INF1  STORE
    INF2  STORE
    
    INF1 --> MON
    INF2 --> MON
    GW --> LOG
    MON --> ADMIN

7.3 Ukrainian Infrastructure Adaptations

8. Integration Testing Protocol

8.1 Test Categories

8.2 Test Execution Checklist

9. Deployment Roadmap

gantt
    title ScanLab AI Integration Deployment Timeline
    dateFormat  YYYY-MM-DD
    
    section Infrastructure
    Hardware procurement     :infra1, 2026-03-01, 4w
    Network configuration    :infra2, after infra1, 2w
    Server deployment        :infra3, after infra2, 2w
    
    section Software
    Gateway installation     :sw1, after infra3, 1w
    PACS integration config  :sw2, after sw1, 2w
    Inference node setup     :sw3, after sw1, 2w
    Model deployment         :sw4, after sw3, 1w
    
    section Testing
    Integration testing      :test1, after sw4, 3w
    Performance testing      :test2, after test1, 2w
    Security audit           :test3, after test1, 2w
    
    section Validation
    Clinical validation      :val1, after test2, 4w
    User acceptance testing  :val2, after val1, 2w
    
    section Go-Live
    Soft launch (limited)    :go1, after val2, 2w
    Full production          :go2, after go1, 1w

10. References

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2. Trivedi, H., et al. "A DICOM Framework for Machine Learning and Processing Pipelines Against Real-time Radiology Images." Journal of Digital Imaging 34(4): 1005-1013, 2021. DOI: 10.1007/s10278-021-00491-w
3. DICOM Standards Committee. "PS3.18 Web Services." DICOM Standard, 2024c. https://www.dicomstandard.org/current/
4. HL7 International. "FHIR R4 Specification - ImagingStudy Resource." https://www.hl7.org/fhir/R4/imagingstudy.html
5. IHE Radiology Technical Committee. "AI Workflow for Imaging (AI-WI) Profile." IHE Radiology Technical Framework Supplement, Trial Implementation, 2024.
6. IHE Radiology Technical Committee. "AI Results (AIR) Profile." IHE Radiology Technical Framework Supplement, Trial Implementation, 2025.
7. NVIDIA Corporation. "Clara Deploy SDK Documentation." https://docs.nvidia.com/clara/
8. Project MONAI. "MONAI Deploy App SDK Documentation." https://docs.monai.io/projects/monai-deploy-app-sdk/
9. Pianykh, O.S. "DICOMweb: Background and Application of the Web Standard for Medical Imaging." Journal of Digital Imaging 31(3): 321-330, 2018. DOI: 10.1007/s10278-018-0075-8
10. Law of Ukraine No. 2297-VI "On Personal Data Protection." Verkhovna Rada of Ukraine, 2010.
11. Thrall, J.H., et al. "Artificial Intelligence and Machine Learning in Radiology: Opportunities, Challenges, Pitfalls, and Criteria for Success." Journal of the American College of Radiology 15(3): 504-508, 2018. DOI: 10.1016/j.jacr.2017.12.026
12. Google Cloud. "Cloud Healthcare API - DICOMweb." https://cloud.google.com/healthcare-api/docs/dicomweb
13. Microsoft. "Azure Health Data Services - DICOM Service." https://learn.microsoft.com/azure/healthcare-apis/dicom/
14. AWS. "Amazon HealthLake Imaging." https://aws.amazon.com/healthlake/imaging/
15. Langlotz, C.P., et al. "A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging." Radiology 291(3): 781-791, 2019. DOI: 10.1148/radiol.2019190613
16. RSNA. "Imaging AI in Practice." https://www.rsna.org/ai-imaging
17. Order No. 1110 of the Ministry of Health of Ukraine "On Electronic Health Records." MHSU, 2023.
18. Clunie, D.A. "DICOM Structured Reporting." PixelMed Publishing, 2nd Edition, 2022.
19. Federov, A., et al. "DICOM for Quantitative Imaging Biomarker Development." PeerJ 4: e2057, 2016. DOI: 10.7717/peerj.2057
20. Kohli, M., et al. "Implementing Machine Learning in Radiology Practice and Research." AJR American Journal of Roentgenology 208(4): 754-760, 2017. DOI: 10.2214/AJR.16.17224
21. Harvey, H.B., et al. "How the FDA Regulates AI." Academic Radiology 27(1): 58-61, 2020. DOI: 10.1016/j.acra.2019.09.017
22. European Commission. "Regulation (EU) 2017/745 on Medical Devices (MDR)." Official Journal of the European Union, 2017.
23. IHE International. "IT Infrastructure Technical Framework." https://www.ihe.net/Technical_Frameworks/
24. RFC 6749 "The OAuth 2.0 Authorization Framework." IETF, 2012.
25. RFC 8446 "The Transport Layer Security (TLS) Protocol Version 1.3." IETF, 2018.

11. Appendix: Quick Reference Cards

A.1 DICOMweb Endpoint Summary

A.2 FHIR Resource Endpoints

Document Version: 1.0 | Last Updated: February 2026
Classification: Technical Specification | Status: Approved for Implementation
© 2026 Stabilarity Hub | Odessa National Polytechnic University