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- Technical University of MunichDE99 papers
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6 papers · 1 filter
Multi-step retrieval and reasoning improves radiology question answering with large language models
Sebastian Wind, Jeta Sopa, Daniel Truhn +9
Clinical decision-making in radiology increasingly benefits from artificial intelligence (AI), particularly through large language models (LLMs). However, traditional retrieval-aug…
Large Language Models-Enabled Digital Twins for Precision Medicine in Rare Gynecological Tumors
Jacqueline Lammert, Nicole Pfarr, Leonid Kuligin +16
Rare gynecological tumors (RGTs) present major clinical challenges due to their low incidence and heterogeneity. The lack of clear guidelines leads to suboptimal management and poo…
RadioRAG: Online Retrieval-augmented Generation for Radiology Question Answering
Soroosh Tayebi Arasteh, Mahshad Lotfinia, Keno Bressem +7
Large language models (LLMs) often generate outdated or inaccurate information based on static training datasets. Retrieval-augmented generation (RAG) mitigates this by integrating…
Evaluation of GPT-4 for chest X-ray impression generation: A reader study on performance and perception
Sebastian Ziegelmayer, Alexander W. Marka, Nicolas Lenhart +7
The remarkable generative capabilities of multimodal foundation models are currently being explored for a variety of applications. Generating radiological impressions is a challeng…
MEDBERT.de: A Comprehensive German BERT Model for the Medical Domain
Keno K. Bressem, Jens-Michalis Papaioannou, Paul Grundmann +12
This paper presents medBERTde, a pre-trained German BERT model specifically designed for the German medical domain. The model has been trained on a large corpus of 4.7 Million Germ…
GottBERT: a pure German Language Model
Raphael Scheible, Johann Frei, Fabian Thomczyk +6
Pre-trained language models have significantly advanced natural language processing (NLP), especially with the introduction of BERT and its optimized version, RoBERTa. While initia…