Segmed De-ID, an LLM-Based Data De-Identification Playground
This prototype makes use of Open AI’s davinci model to identify and subsequently redact potential protected health information (PHI) or personal identifying information (PII) within the provided input. The output will be a de-identified version of the original input, where both direct and indirect identifiers - like those mentioned above - have been redacted. This ensures that the resulting output is HIPAA compliant, and We encourage you to test and play around with this tool - no input or output data is stored or saved. Feedback is welcomed - let us know what you think either at…
In plain words
Segmed De-ID is a prototype tool that uses OpenAI's language model to identify and redact protected health information and personally identifiable information from text. Users input content and receive a de-identified version with direct and indirect identifiers removed, designed to meet HIPAA compliance requirements. The tool does not store input or output data and is intended for testing and experimentation with de-identification workflows.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
This prototype makes use of Open AI’s davinci model to identify and subsequently redact potential protected health information (PHI) or personal identifying information (PII) within the provided input. The output will be a de-identified version of the original input, where both direct and indirect identifiers - like those mentioned above - have been redacted. This ensures that the resulting output is HIPAA compliant, and We encourage you to test and play around with this tool - no input or output data is stored or saved. Feedback is welcomed - let us know what you think either at [email protected] or via the Typeform below.
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