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The digitization of healthcare has facilitated the sharing and re-using ofmedical data but has also raised concerns about confidentiality and privacy.HIPAA (Health Insurance Portability and Accountability Act) mandates removingre-identifying information before the dissemination of medical records. Thus,effective and efficient solutions for de-identifying medical data, especiallythose in free-text forms, are highly needed. While various computer-assistedde-identification methods, including both rule-based and learning-based, havebeen developed and used in prior practice, such solutions still lackgeneralizability or need to be fine-tuned according to different scenarios,significantly imposing restrictions in wider use. The advancement of largelanguage models (LLM), such as ChatGPT and GPT-4, have shown great potential inprocessing text data in the medical domain with zero-shot in-context learning,especially in the task of privacy protection, as these models can identifyconfidential information by their powerful named entity recognition (NER)capability. In this work, we developed a novel GPT4-enabled de-identificationframework (``DeID-GPT") to automatically identify and remove the identifyinginformation. Compared to existing commonly used medical text datade-identification methods, our developed DeID-GPT showed the highest accuracyand remarkable reliability in masking private information from the unstructuredmedical 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 textdata processing and de-identification, which provides insights for furtherresearch and solution development on the use of LLMs such as ChatGPT/GPT-4 inhealthcare. Codes and benchmarking data information are available athttps://github.com/yhydhx/ChatGPT-API.

DeID-GPT: Zero-shot Medical Text De-Identification by GPT-4