AI Strategy & MLOps for Healthcare
OraDigit documents responsible engineering foundations for future healthcare-model evaluation. Production deployment, monitoring, regulatory compliance, and institutional integration require separate project-specific evidence and approval.
AI Built for Clinical Reality
- Model Deployment Pipelines
Secure workflows for validating, packaging, and pushing models into production — aligned with clinical systems like PACS, RIS, and EMR. - Monitoring & Drift Detection
Track model performance over time, detect drift in imaging protocols, and trigger retraining pipelines. - Auditability & Governance
Maintain version control, model lineage, and approval logs to meet regulatory and institutional requirements. - Dev-Test-Prod Environments
Build reproducible environments with isolated test beds and CI/CD for AI workflows. - Human-in-the-Loop (HITL)
Enable expert review, override, and validation feedback in the AI lifecycle — vital for high-stakes medical applications.
Strategic Guidance
- AI Roadmap Design
Define clear goals, timelines, and infrastructure plans aligned with your department’s needs. - Governance Requirements
Identify applicable privacy, security, regulatory, and institutional requirements with qualified reviewers before implementation. - MLOps Platforms & Tools
Support for MLflow, Kubernetes, GitHub Actions, and hybrid cloud deployment options. - Clinician-Engineer Collaboration
Bridge the gap between technical development and real-world medical workflows.