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MedDeID is an on-premises framework for de-identifying clinical text using real or synthetic training data, achieving high accuracy in detecting personally identifiable information with minimal over-redaction.
This study demonstrates that large language models with institution-specific prompting can recover protected health information missed by existing de-identification systems, enhancing data privacy compliance in electronic health records.
This paper evaluates whether structure-preserving de-identification via surrogate substitution maintains PHI detectability across multiple detectors, using equivalence testing on 57k paired spans across 7 languages and 11 detectors.
Kimi K3 AI model successfully reads a chest X-ray after OpenMed removes all 23 patient identifiers from the DICOM data, ensuring privacy. The model correctly identifies a left-sided whiteout.
OpenMed 1.8 is an Apache-2.0 toolkit for clinical de-identification that runs entirely locally, with new support for Android, iOS, and browser platforms, and invites community contributions for version 1.9.
A collection of 650+ Apache-2.0 licensed biomedical NER and de-identification models that run on-device via MLX, achieving 30-40x faster inference than PyTorch-CPU on an M3 Max with identical outputs.
This paper proposes a fully local AI cascade for de-identifying educational dialogue, combining a recall-first candidate proposer with a contextual Redact/Keep reviewer. The approach achieves high accuracy without sending data to external APIs, outperforming both smaller local models and commercial APIs on math tutoring transcripts.
Meddies PII is an open multilingual model and dataset for clinical text de-identification, designed to remove patient identifiers while preserving clinical facts. It uses synthetic data generated with dynamic prompting to handle diverse real-world formats.