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AI service — AI Medical

Medical record extraction

Turn clinical documents into structured data — conditions, medications, dosages and coding candidates — run as a managed AI pipeline in our GPU clusters, with a dedicated private cluster or an on-premise option where records may not leave the building.

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.

A letter, a discharge summary or a scanned form goes in; fields come out. Conditions with their codes, medications with dose and route, results with units, dates. Every field carries the sentence it came from, so a coder or clinician can verify it without reading the document end to end.

Context is handled explicitly: a mention that is negated, historical, hypothetical or about a family member is marked as such rather than extracted as a current diagnosis. Scanned and faxed material passes through a layout-aware reading step first, so tables and forms survive as tables and forms.

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 record extraction 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 Medical record extraction →

Common use cases

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