AI belongs inside the control plane, not outside governance
AI is most useful when it accelerates a workflow the organization already understands, such as prioritization, summarization, or draft generation. It becomes unsafe when it bypasses the workflow and publishes directly into the clinical record without the orchestrator controlling the boundary.
AI as one stage in a governed workflow
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Orchestrating clinical generative AI workflows using AWS Step Functions
AWS healthcare blog showing how Step Functions can coordinate context preparation, model execution, and review-aware flows.
Read the clinical AI orchestration patternHuman review gates should define both publish and remediation paths
The key design choice is not whether humans should review at all. It is where they review, what evidence they see, what outcome they can return, and what the state machine does with that result. The orchestrator should make approval, rejection, and rework equally explicit.
- Present the reviewer with the draft output and the evidence used to generate it.
- Capture an explicit approve or reject outcome rather than relying on passive observation.
- Resume the workflow into either publish or remediation states.
- Persist audit evidence for both accepted and rejected outputs.
Waiting for a callback with task token
AWS documentation for the callback pattern used when an external reviewer application controls the decision point.
Review callback approvalsHuman approval tutorial
AWS tutorial for explicit human approval stages that map well to reviewer-led clinical publication control.
Review the approval tutorialKnowledge Check
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