Deploying AI in Production – Healthcare-Specific Challenges

1. Technical Deployment Considerations

Dockerize every AI model and its dependencies to maintain reproducibility across testing and production.

Kubernetes orchestration enables autoscaling, zero-downtime deployments, and container health monitoring.

Use API gateways like NGINX or Kong to route traffic efficiently and secure endpoints via rate limiting and request validation.

1. Security and Compliance Requirements

Healthcare AI systems must comply with privacy standards like HIPAA (USA), GDPR (EU), and NDHM (India). This means:

Encrypt data at rest using AES-256 and in transit using TLS 1.2+.

Enforce multi-level RBAC, ensuring only clinicians or approved users access sensitive data.

Maintain audit trails to track every access and inference request. This helps in case of data breaches or compliance audits.

1. Monitoring and Maintenance Strategies

Use Prometheus for system-level metrics (CPU, memory, request rate).

Use Grafana dashboards to visualize and alert on abnormal spikes or downtime.

For model monitoring, track:

• Input data distribution drift.

• Output score confidence.

• False positives/negatives.

Maintenance Plan:

• Monthly retraining jobs.

• Security patches on containers.

• Weekly backups and validation checks on restore functionality.