Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Comprehensive testing of large language models for extraction of structured data in pathology
20
Zitationen
7
Autoren
2025
Jahr
Abstract
Open-source language models demonstrate comparable performance to proprietary solutions in structuring pathology report data. This finding has significant implications for healthcare institutions seeking cost-effective, privacy-preserving data structuring solutions. The variations in model performance across different configurations provide valuable insights for practical deployment in pathology departments. Our publicly available bilingual dataset serves as both a benchmark and a resource for future research.
Ähnliche Arbeiten
A survey on deep learning in medical image analysis
2017 · 13.617 Zit.
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.224 Zit.
A survey on Image Data Augmentation for Deep Learning
2019 · 11.840 Zit.
QuPath: Open source software for digital pathology image analysis
2017 · 8.208 Zit.
Radiomics: Images Are More than Pictures, They Are Data
2015 · 8.030 Zit.