On-premises AI in hospitals: What local deployment means
An introduction to local AI infrastructure, on-site data processing, and integration with existing clinical systems.
On premises is more than a server location
In an on-premises deployment, the platform and AI models run within the hospital’s designated infrastructure. Processing and storage can therefore take place where the clinical data already resides.
For a hospital, the physical location is only one part of the picture. Clearly defined system boundaries, access paths, responsibilities, and the components required for ongoing operation matter just as much.
From the data center into the local hospital network
The central platform runs in a designated server environment on site. From there, it can connect to relevant data sources and clinical systems within the hospital network.
Users access the available functions through existing devices. A separate installation on every desktop, tablet, or smartphone is not required.
- Server environment within the hospital
- Connection to relevant systems and data sources
- Access through existing devices on the local network
Independent of a public cloud
A local architecture can be designed so that clinical operation does not require processing by a public cloud provider. A connection to the public internet is not inherently required for using the platform within the local hospital network.
Any connections intended for maintenance, updates, or other operational processes are defined technically and organizationally for the specific installation. This separation is an essential part of a sovereign operating model.
Local operation still needs good interfaces
On premises does not mean isolated. Clinical AI becomes useful when it can obtain information from existing systems and return results to the intended workflows.
Established standards such as HL7 FHIR including ISiK and PACS/DICOM can be used alongside individually implemented connections to hospital and practice information systems.
The target architecture is developed with the institution
Hospitals differ in infrastructure, system landscape, and organizational requirements. A local AI platform should therefore not be treated as a rigid package.
The process starts with a technical assessment and a clearly defined clinical use case. From there, the required compute resources, interfaces, access paths, and responsibilities can be derived for the specific deployment.
Data sovereignty becomes a technical decision
Sovereignty remains abstract until it is clear where data is processed and who controls the operating environment. A local operating model turns these questions into concrete architectural decisions.
LLMedi therefore follows an approach in which clinical AI can run entirely on premises and the operating model is clearly defined for each installation.
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