A local cascade framework for educational dialogue de-identification reaches 0.958 macro F1 on math tutoring transcripts, outperforming same-family LLM-only and commercial baselines while remaining fully on-device.
De-identification of clinical free text using natural language processing: A systematic review of current approaches
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SHIELD is a new diverse clinical note dataset paired with distilled small language models that achieve 0.89 span-level precision and 0.88 recall for on-premise PHI de-identification.
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Redact or Keep? A Fully Local AI Cascade for Educational Dialogue De-Identification
A local cascade framework for educational dialogue de-identification reaches 0.958 macro F1 on math tutoring transcripts, outperforming same-family LLM-only and commercial baselines while remaining fully on-device.
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SHIELD: A Diverse Clinical Note Dataset and Distilled Small Language Models for Enterprise-Scale De-identification
SHIELD is a new diverse clinical note dataset paired with distilled small language models that achieve 0.89 span-level precision and 0.88 recall for on-premise PHI de-identification.