On-Premises HPC and AI for Medical and Clinical Workflows

Research, decision support and medical device boundaries

Define intended use before selecting software. Exploratory research, retrospective validation, clinician-facing decision support and autonomous clinical action have different evidence and regulatory obligations. The same model weights can sit in different governance categories depending on how outputs are used.

Keep research sandboxes separated from operational integrations. Require human review, versioned models and a controlled rollback for any output presented in a care pathway.

Platform choices

X1440 AxiRelay supports several smaller imaging/NLP models or service replicas across four 24GB GPUs. X1160 AxiAnvil provides one 96GB GPU for a large imaging model or private clinical LLM with simple memory semantics. X2260 AxiCrucible provides two physical nodes and four 96GB GPUs for multi-user research, resilience and separated validation/production capacity.

Clinical imaging stack

MONAI supplies healthcare-imaging transforms, networks and evaluation patterns; MONAI Label supports assisted annotation; MONAI Deploy shows how an application can receive DICOM, execute inference and emit clinical-format results. Put de-identification, routing and validation around the model. Preserve source identifiers only in authorised systems and test every modality, scanner and protocol in scope.

Use local NVMe for transient series assembly and protected storage for governed datasets and model evidence. Do not retain duplicate patient data by accident through logs or failed-job directories.

Clinical language and RAG

Private RAG can retrieve from approved policies, formularies or local knowledge while the model remains on site. Build an ingestion pipeline that preserves document owner, effective date, version and access group. Return citations and source dates with answers; abstain when evidence is missing. Clinical prompts and outputs may themselves contain patient data and need the same controls as source systems.

Performance and safety evidence

Test accuracy and calibration by site, scanner, population and clinically meaningful subgroup. Measure latency at realistic concurrency and input size, including transfer from clinical systems. Monitor drift, missing inputs, service errors and human overrides. A GPU benchmark does not replace prospective workflow evaluation.

Deployment blueprint

1. Agree intended use, data controller, risk owner and success measures.
2. Build an isolated retrospective dataset and immutable model package.
3. Validate DICOM/text interfaces without writing to live clinical systems.
4. Run shadow mode and compare against expert review.
5. Introduce controlled users, monitoring, downtime and rollback.
6. Revalidate every material model, dependency or workflow change.