Researchers have conducted a comparative study on methods for de-identifying Dutch clinical notes to protect patient privacy while allowing for data reuse. The study evaluated traditional methods like differential privacy (DP) and named entity recognition (NER) alongside newer approaches using large language models (LLMs). Findings indicate that DP mechanisms alone significantly reduce data utility, but combining them with LLM-based preprocessing offers a superior balance between privacy and usefulness for clinical text de-identification. AI
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IMPACT New hybrid approaches combining LLMs with differential privacy may improve the utility of de-identified clinical data for research.
RANK_REASON Academic paper evaluating privacy-preserving techniques for clinical text de-identification.
