AI service — AI Medical · Clinical language
Clinical de-identification
Remove protected health information from notes, structured fields and DICOM studies before data is shared or used for research.
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.
De-identification is the gate in front of every secondary use of clinical data. These models find and remove the eighteen safe-harbour categories from free text, and detect burned-in text on images that header scrubbing alone will miss.
Documents and studies arrive from your systems and come back de-identified, with a separate record of what was removed and under which rule. That record is what makes a release defensible, and it never leaves your governance.
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 clinical de-identification 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
- Research data release — De-identify a corpus before it is shared with a research partner.
- Imaging export — Scrub DICOM headers and detect burned-in PHI on pixel data.
- Vendor and audit sharing — Produce a de-identified extract with a documented removal trail.
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