Towards Fair and Efficient De-identification: Quantifying the Efficiency and Generalizability of De-identification Approaches

Challenge:	a recent study has not examined their generalizability between formats, cultures, and genders.
Approach:	They evaluate large language models (LLMs) and small LLMs at clinical de-identification . they show that smaller models achieve comparable performance while substantially reducing inference cost .
Outcome:	The proposed models outperform larger models in de-identification tasks with limited data . the models can be fine-tuned with limited datasets to outperformed larger models .

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