Skip to main content

AI service — AI Medical

Chest X-ray reporting support

Rank a reading queue and draft structured findings for chest radiographs in our GPU clusters — sized for a department backlog or a national screening programme, with a dedicated private cluster or an on-premise option where required.

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.

Two outputs, both aimed at reader time. Probabilities for a list of common findings let you order a worklist so likely-abnormal studies are read first. A draft findings paragraph gives the radiologist something to correct rather than compose. Where the model supports grounding, each statement is tied to the region of the image behind it, so a claim can be checked against the pixels in seconds.

What comes out is a draft and a structured findings record, never a signed report. The radiologist stays responsible for the diagnosis; the service exists to get them to it faster.

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 chest x-ray reporting support 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.

AI models for Chest X-ray reporting support →

Common use cases

More AI Medical services