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Use cases · Healthcare

Anonymize patient reports before using an AI

Anonymize any document before it reaches an AI. Nothing leaves your device. Discharge summaries, referral letters and notes keep their clinical content; the identifiers become labels.

  • 0 network requests
  • Works with Wi‑Fi off
  • Free, no account
  • Restore names afterwards
Anonymize your document here

Why it matters

Why clinicians anonymize first

  • Health data is a special category

    GDPR and KVKK treat health information as sensitive. A summary request should not carry a patient's name, ID and date of birth to an external service.

  • The clinical content is what matters

    Symptoms, findings, medication and plan are what you want summarized or translated. The patient can be Patient 1.

  • Identifiers hide in headers and footers

    Hospital letterheads, protocol numbers and physician names sit in headers, footers and tables. All of them are scanned in DOCX and PPTX.

  • You restore the identity locally

    The mapping table stays on your device. Restore the names in the answer with the de-anonymizer when the text goes back into the record.

How to do it

Four steps, all in your browser

  1. 1

    Upload the file or paste the text

    Word, PDF, PowerPoint, Excel, CSV, JSON, HTML, Markdown or plain text. Up to 10 files, 50 MB each. The file is opened in your browser tab and never sent anywhere.

  2. 2

    Choose the rule

    Decide how each category is rewritten: labels like Person 1 / Company A, realistic fake values, masking or redaction. Optionally enable smart detection and add words to your dictionary.

  3. 3

    Review what will change

    Names, companies, emails, phone numbers, IDs and IBANs are listed with their replacements. Turn items off, add missed ones, correct a category. Nothing changes until you confirm.

  4. 4

    Download and paste into the AI

    You get the same file back, a Markdown version to paste into the chat, and a mapping table. Paste the AI's answer into the de-anonymizer to put the original names back.

What to replace

Checklist for this domain

The tool detects most of these automatically. Add the rest to your dictionary so they are replaced consistently.

  • Patient names and initials
  • Dates of birth and admission dates
  • National ID and insurance numbers
  • Protocol, file and sample numbers
  • Hospital, clinic and department names
  • Physician and staff names
  • Addresses and phone numbers
  • Relatives' names in the history
  • Rare conditions combined with locations, if identifying

Before and after

What the AI sees

Original

Ali Yılmaz (TC 10000000146, born 03.02.1958) was admitted to Acıbadem Maslak on 12.01.2026 with chest pain. Dr. Zeynep Kurt ordered an ECG.

Anonymized

Person 1 (TC ID 1, born Date 1) was admitted to Organization A on Date 2 with chest pain. Dr. Person 2 ordered an ECG.

Try it now

Anonymize your document here

Nothing is uploaded. Switch Wi‑Fi off if you want to check.

  1. 1Upload
  2. 2Choose rule
  3. 3Review
  4. 4Download

Upload

Drop files here or choose

Up to 10 files · 50 MB each · files stay on your device

or
  • docx
  • pptx
  • xlsx
  • pdf
  • txt
  • md
  • csv
  • json
  • html

Old .doc, .ppt and .xls files: save them as .docx, .pptx or .xlsx first.

0 network requests during processing

Questions

Common questions

Are national ID numbers validated?

Yes. TC Kimlik No and similar numbers are checked with their checksum, which keeps false positives low.

Can I keep the dates but remove the names?

Yes. Categories are toggled individually. Keep dates on if the timeline matters and off if it identifies the patient.

What about scanned reports?

Scanned PDFs without a text layer are not supported. Run OCR first, then anonymize the text or DOCX.

Does the report leave the device?

No. Everything runs in your browser; 0 network requests during processing.

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