A practical healthcare AI lifecycle is:
Build → Validate → Deploy → Monitor → Detect change → Reassess → Improve
This is important because real-world data and workflows can change after deployment.
For agentic AI, monitoring should also include workflow behavior, tool usage, policy changes, and escalation patterns.
The key principle is simple:
A model is not permanently validated.
Its performance must continue to be earned in the environment where it operates.
I am open to remote roles globally.
Top comments (0)