The authors built and benchmarked a modular, on-premises multi-agent deidentification system across 16 orchestrator configurations, using 250 MIMIC-IV records with synthetically injected identifiers alongside head CT, face images, handwriting, audio and German clinical text. The best local configuration reached per-identifier sensitivity of 99.82% (95% CI 99.76-99.88) with clinical preservation of 99.61% per file, and matched a proprietary model on sensitivity with higher specificity. On the institution's own data, per-patient sensitivity was 100%.
Why it is interesting: Deidentification that runs inside the hospital and keeps clinical content intact is the bottleneck for reusing multimodal records in research.