The digitization of healthcare has facilitated the sharing and re-using of medical data but has also raised concerns about confidentiality and privacy. HIPAA (Health Insurance Portability and Accountability Act) mandates removing re-identifying information before the dissemination of medical records. Thus, effective and efficient solutions for de-identifying medical data, especially those in free-text forms, are highly needed. While various computer-assisted de-identification methods, including both rule-based and learning-based, have been developed and used in prior practice, such solutions still lack generalizability or need to be fine-tuned according to different scenarios, significantly imposing restrictions in wider use. The advancement of large language models (LLM), such as ChatGPT and GPT-4, have shown great potential in processing text data in the medical domain with zero-shot in-context learning, especially in the task of privacy protection, as these models can identify confidential information by their powerful named entity recognition (NER) capability. In this work, we developed a novel GPT4-enabled de-identification framework ("DeID-GPT") to automatically identify and remove the identifying information. Compared to existing commonly used medical text data de-identification methods, our developed DeID-GPT showed the highest accuracy and remarkable reliability in masking private information from the unstructured medical text while preserving the original structure and meaning of the text. This study is one of the earliest to utilize ChatGPT and GPT-4 for medical text data processing and de-identification, which provides insights for further research and solution development on the use of LLMs such as ChatGPT/GPT-4 in healthcare. Codes and benchmarking data information are available at https://github.com/yhydhx/ChatGPT-API.

翻译：医疗保健的数字化促进了医学数据的共享与重用，但也引发了对保密性和隐私的担忧。HIPAA（健康保险便携性与责任法案）要求在传播医疗记录前去除再识别信息。因此，迫切需要高效且有效的医学数据去标识化方案，尤其是针对自由文本形式的数据。尽管包括基于规则和基于学习的方法在内的多种计算机辅助去标识化方法已在先前实践中得到开发和应用，但这些方案仍缺乏泛化能力或需要根据不同的场景进行微调，极大限制了其更广泛的应用。大型语言模型（LLM）的进步，例如ChatGPT和GPT-4，在零样本上下文学习处理医学领域文本数据方面展现出巨大潜力，特别是在隐私保护任务中，因为这些模型可以通过其强大的命名实体识别（NER）能力识别机密信息。在本研究中，我们开发了一种新颖的基于GPT-4的去标识化框架（"DeID-GPT"），用于自动识别和移除标识信息。与现有的常用医学文本数据去标识化方法相比，我们开发的DeID-GPT在从非结构化医学文本中屏蔽私有信息方面表现出最高的准确性和卓越的可靠性，同时保留了文本的原始结构和含义。本研究是首批利用ChatGPT和GPT-4进行医学文本数据处理和去标识化的研究之一，为后续利用ChatGPT/GPT-4等LLM在医疗保健领域的研究及解决方案开发提供了见解。代码和基准数据信息可在https://github.com/yhydhx/ChatGPT-API获取。