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.
Common use cases
- Worklist ordering — Rank a reading queue so likely-abnormal studies reach a radiologist sooner.
- Report drafting — Produce a first-pass findings paragraph for a reader to correct and sign.
- Screening programmes — Process high volumes of normal studies to concentrate reader time where it matters.
- Retrospective review — Re-read an archive for a specific finding to build a research or audit cohort.
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