Unlocking the Potential of Large Language Models for Clinical Text Anonymization: A Comparative Study

arXiv:2406.00062v1 Announce Type: cross
Abstract: Automated clinical text anonymization has the potential to unlock the widespread sharing of textual health data for secondary usage while assuring patient privacy and safety. Despite the proposal of many complex and theoretically successful anonymization solutions in literature, these techniques remain flawed. As such, clinical institutions are still reluctant to apply them for open access to their data. Recent advances in developing Large Language Models (LLMs) pose a promising opportunity to further the field, given …

abstract anonymization arxiv automated clinical comparative study cs.ai cs.cl cs.lg data health health data language language models large language large language models literature patient potential privacy proposal safety solutions study text textual type usage while