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
their capability to perform various tasks. This paper proposes six new
evaluation metrics tailored to the challenges of generative anonymization with
LLMs. Moreover, we present a comparative study of LLM-based methods, testing
them against two baseline techniques. Our results establish LLM-based models as
a reliable alternative to common approaches, paving the way toward trustworthy
anonymization of clinical text.

自动化临床文本匿名化有潜力在保护患者隐私和安全的同时促进医疗数据的广泛共享，本论文提出了基于大型语言模型的新评估指标，并通过与基准技术的比较研究证明了大型语言模型在临床文本匿名化领域的可靠性，为可信的临床文本匿名化铺平了道路。