Development of a Synthetic Oncology Pathology Dataset for Large Language Model Evaluation in Medical Text Classification
The dataset provides a structured, clinically relevant benchmark for evaluating LLM performance in pathology text classification. It enables AI model assessment without compromising data privacy, paving the way for scalable and ethical AI-driven oncology documentation.
Authors
- Patricia Gscheidlinger
- Werner O. Hackl (ORCID: https://orcid.org/0000-0002-2688-3396)
- Sabrina Neururer (ORCID: https://orcid.org/0000-0003-3827-4376)
- Marco Schweitzer (ORCID: https://orcid.org/0000-0001-8745-909X)
- Bernhard Pfeifer (ORCID: https://orcid.org/0000-0003-1507-1015)
- Martin Danler
- Stefan Richter (ORCID: https://orcid.org/0000-0003-0620-113X)
- Christoph Hickmann
- Hasan Taha
- Marko Ueberegger
- Helmut Muehlboeck
Institutions
- UMIT - Private Universität für Gesundheitswissenschaften, Medizinische Informatik und Technik (AT)
- Tirol Kliniken (AT)
Publication Details
- Journal
- Studies in health technology and informatics
- Published
- 2025-04-24
- DOI
- https://doi.org/10.3233/shti250191
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00