- System Design Principles of Medical AI
Modular Architecture: Use microservices to isolate AI model serving, data preprocessing, and user interfaces for easier maintenance.
Interoperability: Design APIs to integrate seamlessly with EHR systems using standards like HL7/FHIR.
Latency Sensitivity: Optimize pipelines for sub-second inference where clinical decision time is critical.
Fault Tolerance: Deploy redundant services with automated failover to ensure system availability.
Data Privacy by Design: Implement RBAC, encrypted storage, and transit encryption (TLS) from design stage.
- Scalability Challenges and Solutions
Challenge: High variability in patient data loads during peak hours.
Solution: Use Kubernetes Horizontal Pod Autoscaler to dynamically scale AI inference pods.
Challenge: Maintaining model performance across heterogeneous hospital datasets.
Solution: Incorporate continuous model monitoring and retraining pipelines.
Challenge: Limited hardware resources in on-prem hospital deployments.
Solution: Optimize models using quantization and lightweight frameworks like TensorRT.
- Non-Proprietary Technical Insights
Inference Optimization: Batch small requests using asynchronous processing to reduce API call overhead.
Framework Choices: PyTorch Lightning for rapid model iteration, FastAPI for high-performance serving.
Deployment Strategy: Canary deployments via Kubernetes to test new model versions with minimal risk.
Monitoring Tools: Prometheus and Grafana for real-time system and model performance visualization.