13 Micro-service “Acme AI Solution”
Note
Q: Let’s say I want to build micro-service system architecture deployed to the intranet of local hospital.
Some service may be just backend only (e.g, .NET Web API or Python FastAPI), some may be frontend+backend (e.g., React JS with SPA + Python FastAPI), and some may be pure frontend (e.g., React JS with SPA). Also, databases (SQL, NoSQL) are involve in this picture.
Can this possible? Please give example design of such system architecture for “Acme AI Solution” inc. (a generative AI company for medical use) that deployed to a local hospital.
Absolutely! This is not only possible but a very common architecture pattern for modern healthcare systems. Let me design a comprehensive microservice architecture for “Acme AI Solution” deployed in a hospital intranet.
13.1 System Architecture Overview
┌─────────────────────────────────────── HOSPITAL INTRANET ───────────────────────────────────────┐
│ │
│ ┌────────────────────────────────── PRESENTATION LAYER ─────────────────────────────────┐ │
│ │ │ │
│ │ ┌──────────────┐ ┌──────────────┐ ┌───────────────┐ ┌──────────────┐ │ │
│ │ │ Radiology │ │ Clinical │ │ Admin │ │ Mobile │ │ │
│ │ │ AI Portal │ │ Dashboard │ │ Portal │ │ App │ │ │
│ │ │ (React SPA) │ │ (React SPA) │ │(React+FastAPI)│ │ (Flutter) │ │ │
│ │ └──────┬───────┘ └──────┬───────┘ └──────┬────────┘ └──────┬───────┘ │ │
│ └─────────┼─────────────────┼─────────────────┼──────────────────┼──────────────────────┘ │
│ │ │ │ │ │
│ ┌─────────▼─────────────────▼─────────────────▼──────────────────▼──────────────────────┐ │
│ │ NGINX REVERSE PROXY / API GATEWAY │ │
│ │ (Load Balancing, SSL Termination, Routing) │ │
│ └────────────────────────────────────────┬──────────────────────────────────────────────┘ │
│ │ │
│ ┌────────────────────────────────────────▼──────────────────────────────────────────────┐ │
│ │ SERVICE MESH (Istio/Linkerd) │ │
│ │ (Service Discovery, Circuit Breaker, mTLS) │ │
│ └────────────────────────────────────────┬──────────────────────────────────────────────┘ │
│ │ │
│ ┌────────────────────────────── MICROSERVICES LAYER ───────────────────────────────────┐ │
│ │ │ │
│ │ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ │
│ │ │ AI Inference │ │ Image Analysis │ │ Patient │ │ Notification │ │ │
│ │ │ Service │ │ Service │ │ Service │ │ Service │ │ │
│ │ │ (Python FastAPI)│ │ (.NET Web API) │ │ (Python FastAPI)│ │ (Node.js) │ │ │
│ │ └────────┬────────┘ └────────┬────────┘ └────────┬────────┘ └────────┬────────┘ │ │
│ │ │ │ │ │ │ │
│ │ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ ┌─────────────────┐ │ │
│ │ │ DICOM Router │ │ Report Gen │ │ Authentication │ │ Audit Log │ │ │
│ │ │ Service │ │ Service │ │ Service │ │ Service │ │ │
│ │ │ (Go/C++) │ │ (Python FastAPI)│ │ (.NET Core) │ │ (Python FastAPI)│ │ │
│ │ └────────┬────────┘ └────────┬────────┘ └────────┬────────┘ └────────┬────────┘ │ │
│ └───────────┼────────────────────┼────────────────────┼────────────────────┼───────────┘ │
│ │ │ │ │ │
│ ┌───────────▼────────────────────▼────────────────────▼────────────────────▼────────────┐ │
│ │ MESSAGE BROKER (RabbitMQ/Kafka) │ │
│ │ (Async Communication, Event Streaming) │ │
│ └────────────────────────────────────────┬──────────────────────────────────────────────┘ │
│ │ │
│ ┌────────────────────────────── DATA LAYER ─────────────────────────────────────────────┐ │
│ │ │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌────────────┐ │ │
│ │ │ PostgreSQL │ │ MongoDB │ │ Redis │ │ MinIO/S3 │ │ InfluxDB │ │ │
│ │ │ (Patient │ │ (AI Model │ │ (Cache/ │ │ (Medical │ │ (Metrics) │ │ │
│ │ │ Records) │ │ Results) │ │ Sessions) │ │ Images) │ │ │ │ │
│ │ └─────────────┘ └─────────────┘ └─────────────┘ └─────────────┘ └────────────┘ │ │
│ └───────────────────────────────────────────────────────────────────────────────────────┘ │
│ │
└─────────────────────────────────────────────────────────────────────────────────────────────────┘13.2 Detailed Service Architecture
13.2.1 Frontend Services
┌─────────────────────────────────────────────────────────┐
│ FRONTEND SERVICES │
├─────────────────────────────────────────────────────────┤
│ │
│ ╔═══════════════════════════════════════╗ │
│ ║ Radiology AI Portal (Pure Frontend) ║ │
│ ╟───────────────────────────────────────╢ │
│ ║ • React 18 + TypeScript ║ │
│ ║ • Redux Toolkit (State) ║ │
│ ║ • Material-UI / Ant Design ║ │
│ ║ • DICOM Viewer (Cornerstone.js) ║ │
│ ║ • Nginx serving static files ║ │
│ ╚═══════════════════════════════════════╝ │
│ │
│ ╔═══════════════════════════════════════╗ │
│ ║ Admin Portal (Frontend + Backend) ║ │
│ ╟───────────────────────────────────────╢ │
│ ║ Frontend: ║ │
│ ║ • React SPA ║ │
│ ║ • Admin dashboard components ║ │
│ ║ Backend: ║ │
│ ║ • Python FastAPI ║ │
│ ║ • User management endpoints ║ │
│ ║ • System configuration APIs ║ │
│ ╚═══════════════════════════════════════╝ │
│ │
└─────────────────────────────────────────────────────────┘13.2.2 Backend Microservices
┌─────────────────────────────────────────────────────────┐
│ BACKEND MICROSERVICES │
├─────────────────────────────────────────────────────────┤
│ │
│ ╔═══════════════════════════════════════╗ │
│ ║ AI Inference Service (Python) ║ │
│ ╟───────────────────────────────────────╢ │
│ ║ • FastAPI + Uvicorn ║ │
│ ║ • PyTorch/TensorFlow models ║ │
│ ║ • ONNX Runtime ║ │
│ ║ • Celery for async tasks ║ │
│ ║ • GPU support (CUDA) ║ │
│ ║ Endpoints: ║ │
│ ║ • POST /api/v1/inference/chest-xray ║ │
│ ║ • POST /api/v1/inference/ct-scan ║ │
│ ║ • GET /api/v1/models/status ║ │
│ ╚═══════════════════════════════════════╝ │
│ │
│ ╔═══════════════════════════════════════╗ │
│ ║ Image Analysis Service (.NET) ║ │
│ ╟───────────────────────────────────────╢ │
│ ║ • ASP.NET Core Web API ║ │
│ ║ • ImageSharp for processing ║ │
│ ║ • fo-dicom for DICOM handling ║ │
│ ║ • SignalR for real-time updates ║ │
│ ║ Endpoints: ║ │
│ ║ • POST /api/v1/analyze/dicom ║ │
│ ║ • GET /api/v1/series/{id}/images ║ │
│ ║ • POST /api/v1/preprocess/normalize ║ │
│ ╚═══════════════════════════════════════╝ │
│ │
└─────────────────────────────────────────────────────────┘13.2.3 Docker Compose Configuration Example
# docker-compose.yml
version: '3.8'
services:
# Frontend Services
radiology-portal:
build: ./frontend/radiology-portal
ports:
- "3000:80"
environment:
- API_GATEWAY_URL=http://api-gateway:8080
networks:
- acme-network
# Backend Services
ai-inference-service:
build: ./backend/ai-inference
ports:
- "8001:8000"
environment:
- DATABASE_URL=postgresql://user:pass@postgres:5432/ai_db
- REDIS_URL=redis://redis:6379
- MODEL_PATH=/models
volumes:
- ./models:/models
- ./data:/data
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 1
capabilities: [gpu]
networks:
- acme-network
image-analysis-service:
build: ./backend/image-analysis
ports:
- "8002:80"
environment:
- ConnectionStrings__DefaultConnection=Server=postgres;Database=images_db
- MessageBroker__Host=rabbitmq
networks:
- acme-network
patient-service:
build: ./backend/patient-service
ports:
- "8003:8000"
environment:
- DATABASE_URL=postgresql://user:pass@postgres:5432/patient_db
- FHIR_SERVER_URL=http://hapi-fhir:8080/fhir
networks:
- acme-network
# Databases
postgres:
image: postgres:15-alpine
environment:
- POSTGRES_MULTIPLE_DATABASES=ai_db,patient_db,images_db
- POSTGRES_USER=acme_user
- POSTGRES_PASSWORD=secure_password
volumes:
- postgres_data:/var/lib/postgresql/data
networks:
- acme-network
mongodb:
image: mongo:6.0
environment:
- MONGO_INITDB_ROOT_USERNAME=admin
- MONGO_INITDB_ROOT_PASSWORD=secure_password
volumes:
- mongo_data:/data/db
networks:
- acme-network
redis:
image: redis:7-alpine
command: redis-server --requirepass secure_password
volumes:
- redis_data:/data
networks:
- acme-network
# Message Broker
rabbitmq:
image: rabbitmq:3.12-management
ports:
- "5672:5672"
- "15672:15672"
environment:
- RABBITMQ_DEFAULT_USER=acme
- RABBITMQ_DEFAULT_PASS=secure_password
networks:
- acme-network
# Object Storage
minio:
image: minio/minio:latest
command: server /data --console-address ":9001"
ports:
- "9000:9000"
- "9001:9001"
environment:
- MINIO_ROOT_USER=admin
- MINIO_ROOT_PASSWORD=secure_password
volumes:
- minio_data:/data
networks:
- acme-network
# API Gateway
api-gateway:
image: nginx:alpine
ports:
- "8080:80"
volumes:
- ./nginx/nginx.conf:/etc/nginx/nginx.conf
depends_on:
- ai-inference-service
- image-analysis-service
- patient-service
networks:
- acme-network
networks:
acme-network:
driver: bridge
volumes:
postgres_data:
mongo_data:
redis_data:
minio_data:13.2.4 Service Communication Patterns
┌───────────────────────────────────────────────────────────┐
│ COMMUNICATION PATTERNS │
├───────────────────────────────────────────────────────────┤
│ │
│ Synchronous (REST): │
│ ┌──────────┐ HTTP/REST ┌──────────┐ │
│ │ Frontend │ ◄──────────────────► │ Backend │ │
│ └──────────┘ └──────────┘ │
│ │
│ Asynchronous (Message Queue): │
│ ┌──────────┐ Publish ┌──────────┐ │
│ │ Service A│ ────────────────────►│ RabbitMQ │ │
│ └──────────┘ └────┬─────┘ │
│ │ Subscribe │
│ ▼ │
│ ┌──────────┐ │
│ │ Service B│ │
│ └──────────┘ │
│ │
│ Event Streaming (Kafka): │
│ ┌──────────┐ ┌──────────┐ │
│ │ Producer │─────────────────────►│ Kafka │ │
│ └──────────┘ └────┬─────┘ │
│ ▲ │ │
│ │ ▼ │
│ ┌──────┴───┐ ┌──────────┐ │
│ │ Consumer │◄────────────────────│ Consumer │ │
│ └──────────┘ └──────────┘ │
│ │
└───────────────────────────────────────────────────────────┘13.2.5 Security & Compliance Layer
┌───────────────────────────────────────────────────────────┐
│ SECURITY & COMPLIANCE (HIPAA) │
├───────────────────────────────────────────────────────────┤
│ │
│ • End-to-end encryption (TLS 1.3) │
│ • OAuth 2.0 / SAML 2.0 with hospital Active Directory │
│ • Role-Based Access Control (RBAC) │
│ • Audit logging for all data access │
│ • Data encryption at rest (AES-256) │
│ • Network segmentation (VLANs) │
│ • Regular security scanning (OWASP ZAP, SonarQube) │
│ • HL7 FHIR compliance for interoperability │
│ │
└───────────────────────────────────────────────────────────┘13.2.6 Deployment Strategy
┌───────────────────────────────────────────────────────────┐
│ DEPLOYMENT OPTIONS │
├───────────────────────────────────────────────────────────┤
│ │
│ Option 1: Kubernetes (Recommended for Scale) │
│ ┌────────────────────────────────────────────┐ │
│ │ Hospital Private Cloud / On-Premise │ │
│ │ • Kubernetes cluster (3+ nodes) │ │
│ │ • Helm charts for deployment │ │
│ │ • Istio service mesh │ │
│ │ • Prometheus + Grafana monitoring │ │
│ └────────────────────────────────────────────┘ │
│ │
│ Option 2: Docker Swarm (Simpler) │
│ ┌────────────────────────────────────────────┐ │
│ │ Docker Swarm Cluster │ │
│ │ • 3-5 physical servers │ │
│ │ • Docker stack deploy │ │
│ │ • Portainer for management │ │
│ └────────────────────────────────────────────┘ │
│ │
│ Option 3: Traditional VMs (Legacy Compatible) │
│ ┌────────────────────────────────────────────┐ │
│ │ VMware vSphere / Hyper-V │ │
│ │ • Each service in separate VM │ │
│ │ • Ansible/Terraform for provisioning │ │
│ └────────────────────────────────────────────┘ │
│ │
└───────────────────────────────────────────────────────────┘13.2.7 Integration with Hospital Systems
┌───────────────────────────────────────────────────────────┐
│ HOSPITAL SYSTEM INTEGRATION │
├───────────────────────────────────────────────────────────┤
│ │
│ ┌──────────────┐ ┌──────────────────────┐ │
│ │ PACS │◄───────►│ DICOM Router Service│ │
│ │ System │ DICOM └──────────────────────┘ │
│ └──────────────┘ │
│ │
│ ┌──────────────┐ ┌──────────────────────┐ │
│ │ HIS/EMR │◄───────►│ FHIR Adapter │ │
│ │ (Epic, │ HL7 │ Service │ │
│ │ Cerner) │ FHIR └──────────────────────┘ │
│ └──────────────┘ │
│ │
│ ┌──────────────┐ ┌──────────────────────┐ │
│ │ RIS │◄───────►│ Integration │ │
│ │ │ API │ Service │ │
│ └──────────────┘ └──────────────────────┘ │
│ │
└───────────────────────────────────────────────────────────┘13.3 Key Advantages of This Architecture
- Scalability: Each service can scale independently based on load
- Technology Agnostic: Mix different tech stacks (.NET, Python, Node.js)
- Fault Isolation: One service failure doesn’t bring down the system
- Development Speed: Teams can work independently on different services
- HIPAA Compliance: Built-in security and audit capabilities
- High Availability: Can deploy multiple instances of critical services
- Easy Updates: Rolling updates without system downtime
13.4 Best Practices for Hospital Deployment
- Network Isolation: Keep the system in a separate VLAN
- Backup Strategy: Regular backups of databases and configurations
- Monitoring: Set up comprehensive logging and alerting
- Disaster Recovery: Have a DR plan with RPO/RTO defined
- Performance Testing: Load test with realistic hospital data volumes
- Compliance Documentation: Maintain detailed architecture and security docs
This architecture is production-ready and follows industry best practices for healthcare systems. Would you like me to dive deeper into any specific component.