AI service — AI Medical · Radiology
CT analysis
Turn CT volumes into embeddings, findings, triage scores and retrieval — from whole-body representation models to organ-specific diagnosis.
Send us your data volume, throughput and latency targets and any constraint we should design around. You get a proposed configuration, a benchmark on your own data, and a known cost per unit of work before you commit.
CT is the highest-value and heaviest imaging workload in most hospitals. These models read a volume and return one of three things: an embedding your own endpoints are trained on, a set of findings across anatomy, or a triage score that orders a reading queue.
Volumes arrive from your PACS or a DICOM store and come back as scored findings, masks or embeddings, with the model version recorded per study. Vision-language models over image and report together also index an archive for retrieval by description.
Every output is clinical decision support, not a diagnosis: a qualified professional reviews and signs it. Where clinical use requires regulatory approval in your jurisdiction, that approval remains yours to hold — we provide the infrastructure, the model operations and the audit trail behind it.
What we size for
We build the ct analysis pipeline around the workload you actually have: data format, accuracy target, latency, throughput, concurrency, retention and scheduling. Start with a pilot, then scale production capacity without changing a line of your integration.
Common use cases
- Worklist triage — Score studies so likely-abnormal cases reach a radiologist sooner.
- Retrospective cohorts — Search a CT archive by finding rather than by report text.
- Label-efficient endpoints — Train your own classifier on volume embeddings instead of raw CT.
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