43 citations
- Technical University of MunichDE9 papers
- TUM KlinikumDE9 papers
- Heidelberg UniversityDE6 papers
- University Hospital HeidelbergDE6 papers
- Charité - Universitätsmedizin BerlinDE5 papers
- RWTH Aachen UniversityDE5 papers
- Friedrich-Alexander-Universität Erlangen-NürnbergDE4 papers
- Radboud University Medical CenterNL4 papers
- Radboud University NijmegenNL4 papers
- Else Kröner Fresenius Center for Digital HealthDE3 papers
- German Centre for Cardiovascular ResearchDE3 papers
- Harvard UniversityUS3 papers
Showing cs.CLShow all
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cs.CL2025★ 14 cited
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…
cs.CL2024★ 25 cited
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…
cs.CL2024★ 27 cited
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Large language models (LLMs) often generate outdated or inaccurate information based on static training datasets.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…