SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification
arXiv:2605.03301v2 Announce Type: replace Abstract: 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 (PHI) to cloud APIs. We introduce SHIELD (Synthetic
The proliferation of LLMs and increasing regulatory scrutiny on data privacy, particularly in healthcare, necessitate new approaches to de-identification that balance utility and security.
This development addresses a critical barrier to the ethical and widespread secondary use of clinical data for AI and research, enabling more robust healthcare innovation while complying with data governance.
The availability of a diverse clinical dataset and scaled-down, privacy-preserving AI models alters the landscape for healthcare AI development, allowing more institutions to leverage their data securely.
- · Healthcare AI Developers
- · Hospitals and Healthcare Systems
- · Medical Researchers
- · Patients (through improved AI applications)
- · Cloud API providers relying solely on external processing of PHI
- · Organizations with outdated data de-identification practices
Increased pace of healthcare AI development and deployment within hospitals.
New standards and best practices for secure and private clinical data utilization emerge globally.
Shifts in regulatory frameworks to accommodate enterprise-scale, privacy-preserving AI in sensitive domains like healthcare.
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Read at arXiv cs.CL