Healthcare organizations work with sensitive clinical data subject to strict privacy, residency, and compliance rules. Patient records sit across hospitals, labs, and research institutions, but cannot be shared or centralized. External environments — including cloud platforms and partner systems — must be used carefully, since visibility into raw data or model logic is often unacceptable. Many AI models also contain proprietary methods that organizations do not want to expose. These constraints limit how far AI projects can scale, especially when they require broader datasets or collaboration across institutions.
Super enables hospitals, research groups, and partners to run joint or sensitive AI workloads without transferring patient data or revealing model internals. Each institution keeps data within its own environment while contributing it securely to a shared computation. Workloads can run in cloud or hybrid setups with technical separation from the underlying platform, and proprietary diagnostic models can be deployed inside hospitals without exposing internal logic.
Institutions can adopt and collaborate on AI without weakening privacy boundaries or exposing sensitive assets.
