From clinical decision support to clinical action systems
Design principles for medical AI that moves beyond prediction and begins to change what happens next.
Most medical AI systems end where clinical work begins. They identify a lesion, estimate a risk, summarize a chart, or recommend a next step. A human remains responsible for translating that output into an intervention in the world.
AI-native medicine asks a different design question: what changes when an intelligent system can remain inside the loop—observing the response to an action, adjusting what happens next, and learning across episodes?
The central unit is no longer an input–label pair. It is a clinical episode: state → observation → decision → action → response → outcome, bounded by oversight and the possibility of safe interruption.