deidentifying-multilingual-text

You can de-identify multilingual clinical text by detecting and replacing personally identifiable information on-device. This process preserves privacy by using language-specific PII models and locale-aware surrogates without any external data transmission.

De-identification validates structured national identifiers like DNI, NIR, Steuer-ID, codice fiscale, BSN, CPF, TCKN, and Aadhaar. It uses language-aware patterns and validators to detect and replace these identifiers with locale-appropriate surrogates.

Yes, you can generate locale-aware replacement data for specific regional locales. The process creates realistic surrogate names, dates, phone numbers, and national identifiers that match the source language's formatting and regional characteristics.

On-device PII detection supports multiple languages including German, Spanish, French, Italian, Portuguese, Dutch, Hindi, Telugu, Arabic, Japanese, and Turkish. It applies language-specific models to accurately identify sensitive information in clinical text.

The best way to handle accent normalization is through the de-identification process itself, which applies accent normalization alongside language-specific PII detection. This ensures consistent text processing while replacing sensitive information with native-looking surrogates.

Yes, you need to select a supported language and optionally a regional locale to de-identify clinical text. This requirement ensures the system applies the correct language-specific PII models, national identifier validation, and locale-aware surrogate generation.