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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.Optional, downloads a one-time model and runs locally.

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

A pattern layer runs instantly and locally on the text you paste, catching structured identifiers like SSNs, dates, and phone numbers with no API call and no server round-trip. You can optionally enable an in-browser AI model (a one-time download) to catch names it misses by context. Either way, sensitive clinical text never leaves the device.

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