MedDeID enables locally governed clinical-text de-identification from real or synthetic training data

Excerpt

arXiv:2609.10049v1 Announce Type: new Abstract: Clinical notes contain personally identifiable information (PII), restricting reuse for research and medical AI, especially when data cannot leave an institution. We developed MedDeID, an on-premises framework combining in-house annotation and synthetic-note generation with model training, inference, pseudonymisation and evaluation. On an independently annotated, adjudicated 300-note Dutch hospital benchmark, a hospital-trained compact transformer