Prompt Engineering Strategies for Context-Aware Medical Text Anonymization Using LLMs: Insights from the GraSCCo Corpus

Key Points

• The aim is to explore effective prompt engineering strategies for anonymizing clinical texts using large language models.
• Analyzed the GraSCCo corpus for insights on text anonymization.
• Utilized various prompt engineering techniques to optimize LLM performance.
• Evaluated the effectiveness of LLMs in de-identifying sensitive clinical information.
• Identified key strategies that enhance the anonymization process in clinical texts.
• Demonstrated improved accuracy in de-identification tasks using LLMs compared to traditional methods.