Research Tools & Code·arXiv cs.CL·20h ago

On-premises de-identification lets hospitals train clinical AI without sharing patient data

MedDeID addresses a critical bottleneck in clinical AI: institutions cannot share patient notes for model training without violating privacy constraints. This framework enables hospitals to build de-identification systems entirely on-premises, using either their own annotated data or synthetic notes generated locally. The key finding is that synthetic-only training rivals hospital-specific models on Dutch benchmarks (96.1% vs 98.9% detection), while outperforming on format robustness. This shifts the economics of clinical AI deployment, allowing smaller healthcare systems to participate in research and model development without data egress, potentially unlocking millions of notes currently locked behind compliance walls.

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Explainer

The real finding is not that de-identification works on-premises, but that synthetic-only training closes the performance gap to hospital-specific models. The 1.8 percentage point gap (96.1% vs 98.9%) is small enough that format robustness gains flip the trade-off: institutions may no longer need their own annotated data to deploy effective systems.

This extends a pattern visible across recent work on privacy-preserving collaborative ML. The federated learning framework (TNFL, from September 9) tackled multi-center model training without data centralization by routing updates through trust networks. MedDeID solves a parallel problem for single institutions: avoiding data egress entirely by generating training material locally. Both papers recognize that the bottleneck is not model capacity but institutional friction around data movement. The synthetic-training equivalence here mirrors the robustness engineering approach in NOPE-HYPE (same date), which showed that controllable synthetic data can match real-world performance if sampling and simulation are principled.

If MedDeID's synthetic models maintain the 96%+ detection rate when tested on clinical notes from institutions outside the Dutch healthcare system (different EHR formats, different patient populations, different de-identification conventions), that confirms the approach generalizes. If performance drops below 90% on US or UK data, the synthetic training may be overfitting to Dutch clinical text patterns, limiting adoption beyond its origin context.

Coverage we drew on

A Trust-Network-Based Federated Learning Framework for Multi-Center Aging Clock Prediction · arXiv cs.LG

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