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Ayothedoc
On-device demo

De-identify clinical text, on your device

Paste a clinical note and watch protected health information get removed, entirely in your browser. Nothing is uploaded, nothing leaves your machine.

Runs entirely in your browser. Nothing is uploaded.Loading AI model…

Clinical text

Use synthetic data only. This is a demonstration.

De-identified output

DISCHARGE SUMMARY

Patient: [NAME]    MRN: [MRN]
DOB: [DATE]    SSN: [SSN]
Address: [ADDRESS], Springfield
Phone: [PHONE]    Email: [EMAIL]

Seen by: [NAME] on [DATE].

Mr. [NAME] presented with chronic myeloid leukemia and Type 2 diabetes.
Started on imatinib 400mg daily. Follow-up scheduled for [DATE].
Insurance ID: [ID].
12 identifiers removed
NAME 3MRN 1DATE 3SSN 1ADDRESS 1PHONE 1EMAIL 1ID 1
How this demo works

Processing stays on the device

This page loads a named-entity model into your browser, with a pattern layer for structured identifiers like SSNs and phone numbers, and runs it locally on the text you paste. There is no API call and no server round-trip: sensitive clinical text should not have to leave the device to be de-identified. If the model cannot load, it falls back to pattern detection so the tool still works.

In a real engagement

What we implement for clients

  • Full clinical NER models, not just pattern matching, running on your own infrastructure (on-prem, air-gapped, or mobile).
  • Coverage of the 18 HIPAA Safe Harbor identifiers with configurable confidence thresholds.
  • Format-preserving synthetic replacement, date shifting, and consistent pseudonymisation.
  • Governance: audit reports, redaction previews, and human review on low-confidence spans.
  • Validation against your own documents before anything goes live.

Need on-device de-identification in your product or hospital?

Discuss an On-Device De-identification Project