SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification
De-identification of clinical text is a prerequisite for the secondary use of electronic health records. Existing public benchmarks such as the i2b2 2006 and 2014 corpora are over a decade old and lack the semantic and demographic diversity of modern clinical narratives. Large Language Models (LLMs) reach state-of-the-art zero-shot extraction, but their use at enterprise scale is limited by computational cost and by hospital data governance that restricts sending Protected Health Information (PH
Record details
Published: 5 May 2026
Source: arXiv
Category: Research
Topics: Regulation · Healthcare
Retrieved: 14 July 2026
Related evidence
These records share source-supplied organisations, an exact publisher byline, automatic topics or regions. The reason is shown on every link; related does not mean supporting, agreeing with or verifying this record.
An Empirical Study of Agent Skills for Healthcare: Practice, Gaps, and Governance
arXiv · 4 May 2026
SAIL: Structure-Aware Interpretable Learning for Anatomy-Aligned Post-hoc Explanations in OCT
arXiv · 4 May 2026
MindMelody: A Closed-Loop EEG-Driven System for Personalized Music Intervention
arXiv · 2 May 2026
When RAG Chatbots Expose Their Backend: An Anonymized Case Study of Privacy and Security Risks in Patient-Facing Medical AI
arXiv · 1 May 2026
The Challenges of Balancing AI Compliance and Technological Innovations in Critical Sectors: A Systematic Literature Review
arXiv · 9 May 2026
End-to-End Evaluation and Governance of an EHR-Embedded AI Agent for Clinicians
arXiv · 30 April 2026
How to cite this record
ethics.ai (5 May 2026), “SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification,” evidence record 4991, https://ethics.ai/record/4991 (originally published by arXiv).
Use and limitations
This page is a stable index and citation surface for a source record. ethics.ai did not author the underlying report and has not independently verified every claim. Automatic topics may be imperfect. For consequential use, quote and cite the original publisher.