## 1 Introduction  
### Background  
The recent rise of artificial intelligence (AI) has led to increased research interest in advanced analyses of medical data. However, critical health data are often recorded in free text or in other unstructured forms that are challenging to include in AI model training. Although recent standardization initiatives contribute towards structuring clinical data, free text is still prominent in admission and discharge reports, doctor's letters, transfer letters, clinical notes written by healthcare professionals (HCPs), and many other documents. These texts often contain essential information about a patient's individual condition. For example, during a home visit, a patient's living situation documenting accessibility or cohabitants may be recorded in a nurse's clinical note. While questionnaires can help to structure such information, some details are difficult to capture in a stringent standardized question format. Nuanced differences in health can get lost when developing models that solely rely on structured data. Therefore, providing AI models with comprehensive datasets that include free-text data during training might be essential so that models understand patients in a holistic way, especially for personalized health applications.  
Typically, complex natural language processing (NLP) techniques are required to transform free text into a machine-readable format. However, recent large language models(LLMs) like ChatGPT 4.0 or Google Gemini 1.5 have made handling and fully utilizing free-text data significantly easier. With their inherent capability to interpret natural language, LLMs make this transformation from free text into structured data for machine learning obsolete. They therefore enable the structuring of routinely collected data retrospectively and thus greatly enhance the available data pool without disrupting HCPs' everyday routines (e.g., changing from the usual free-text documentation to a structured questionnaire). However, due to the sensitive nature of medical data, strict regulations and ethical considerations complicate the use of LLM tools that are operated by USA-based companies when data holders are based in Europe. Various examples in the literature have shown the extensive possibilities for re-identification with free-text data [1; 2; 3] and serve as convincing justification for strict regulating frameworks.  
To address the legal challenges and to preserve patient privacy, clinical data are typically de-identified prior to further analyses. While the EU's General Data Protection Regulation (GDPR) [4] considers health data among the most worthy of protection, it does not specify how medical free-text data should be de-identified. The US framework of the Health Insurance Portability and Accountability Act (HIPAA) [5] provides the Safe Harbor Method as a guideline for de-identifying medical data, specifying which personal identifiable information (\"protected health information\", PHI) must be removed to be compliant. While these guidelines were written for tabular data, they can also be applied to free-text data.  
When selecting a specific de-identification algorithm, not only legal requirements but also language and domain must be considered. While large, open-source text corpora exist for other domains (e.g., web-scraped spaCy [6] news, blog and comment corpora), due to the sensitive nature of the clinical domain, no equivalently large collection exists for medical free text. Furthermore, research for German text is notably less common than that for English texts. Therefore, developing methods for de-identifying German medical free text remains a major challenge.