A healthcare machine learning model can estimate probability very well.
But probability alone does not determine action.
Suppose a model predicts a 15% probability of an adverse clinical event.
Whether that should trigger an alert, additional testing, clinical review or no action depends on the decision context.
This is where thresholds become important.
A threshold translates a continuous prediction into an operational decision.
But choosing that threshold is not simply a machine learning optimization problem.
It requires consideration of false positive and false negative consequences, intervention effectiveness, resource availability, patient population and the clinical purpose of the model.
The appropriate threshold for screening may differ from the threshold for treatment escalation.
The threshold for an informational alert may also differ from the threshold for an automated action.
For agentic AI, this distinction becomes particularly important.
An AI system that can execute actions should have decision policies that reflect the consequences of those actions.
Higher-consequence actions should generally require stronger evidence, appropriate authorization and stronger safeguards.
Technical model performance therefore needs to be connected to decision analysis.
The model answers:
“What is the estimated risk?”
The clinical system must also answer:
“What should we do with that risk?”
That second question is where prediction becomes decision intelligence.
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