Wiest Isabella Catharina, Wolf Fabian, Leßmann Marie-Elisabeth, van Treeck Marko, Ferber Dyke, Zhu Jiefu, Boehme Heiko, Bressem Keno K, Ulrich Hannes, Ebert Matthias P, Kather Jakob Nikolas
Department of Medicine II, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
Else Kroener Fresenius Center for Digital Health, Faculty of Medicine and University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany.
NPJ Precis Oncol. 2025 Sep 17;9(1):313. doi: 10.1038/s41698-025-01103-4.
In medical oncology, text data, such as clinical letters or procedure reports, is stored in an unstructured way, making quantitative analysis difficult. Manual review or structured information retrieval is time-consuming and costly, whereas Large Language Models (LLMs) offer new possibilities in natural language processing for structured Information Extraction (IE) from medical free text. This protocol describes a workflow (LLM-AIx) for extracting predefined clinical entities from unstructured oncology text using privacy-preserving LLMs. It addresses a key barrier in clinical research and care by enabling efficient information extraction to support decision-making and large-scale data analysis. It runs on local hospital infrastructure, eliminating the need to transfer patient data externally. We demonstrate its utility on 100 pathology reports from The Cancer Genome Atlas (TCGA) for TNM stage extraction. LLM-AIx requires no programming skills and offers a user-friendly interface for rapid, structured data extraction from clinical free text.
