AI service — AI Medical · Clinical language
Medical image retrieval
Index medical images into a shared image-text space for retrieval, zero-shot classification and prior-study comparison.
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.
An image-text embedding model turns a picture into a vector that sits in the same space as language. That is what allows an archive to be searched by description, a finding to be scored without training a classifier for it, and comparable prior studies to be found for the case on screen.
Images are indexed once and queried thereafter. Indexing runs across your archive as a batch; queries return in milliseconds and cost nothing per search beyond the index you already paid for.
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 medical image retrieval 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
- Search an archive in words — Find images matching a description with no manual tagging.
- Zero-shot finding scores — Score a new finding without assembling a labelled set for it.
- Prior-study comparison — Retrieve visually comparable studies to support a read.
More Clinical language services
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