The first apps stay close to critical care, because that is where the data is richest, the decisions are most frequent and the evidence base already exists. But the underlying method — a risk-aware agent choosing a dose from a patient’s trajectory — is not specific to the ICU.

Later waves move into chronic disease, where the same sequential-dosing problem plays out over months rather than hours, and where personalised support between appointments barely exists today. The clinical question changes; the mathematics does not. What changes with it is the time horizon over which a decision’s consequences appear — and reinforcement learning is precisely the tool built for delayed consequences.

Each app is a distinct clinical problem needing its own evidence, its own validation and its own regulatory approval. What they share is the platform beneath them — and that is the difference between building one medical device and building a category.