Blog
Perspectives on private clinical AI, for hospital leaders, clinicians and security teams.
Build or operate? The hidden cost of building private clinical AI yourself
Open medical models are free to download. Turning them into a safe, private service for clinicians is where the real time, hiring and operational costs lie.
Hybrid AI for hospitals: private where it matters, managed where it helps
Hospitals do not have to choose between sovereignty and leading managed models such as Claude, GPT or Grok. A hybrid architecture lets each task use the right one.
When every hospital runs the same AI, expertise stops being an edge
AI is about to pass through most clinical work. If every provider uses the same assistant, the things that set a leading hospital apart start to disappear.
The break-even point: when owning your clinical AI costs less than renting it
Pay-per-use AI looks cheap at the start. Understanding the break-even point shows when private AI becomes the cheaper option, and how to reach it sooner.
Why a model’s benchmark score tells you little about your hospital
Published scores measure performance on someone else’s data. Before a model supports your clinicians, it needs to prove itself on your cases.
Five questions to ask any clinical AI vendor about data sovereignty
“Your data is safe with us” is a promise. These five questions reveal whether a vendor’s architecture can actually keep it.