"Superhuman performance in urology board questions by an explainable large ..."

Martin J. Hetz et al. (2024)

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DOI: 10.48550/ARXIV.2406.01428

access: open

type: Informal or Other Publication

metadata version: 2024-10-06

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  - journals/corr/abs-2406-01428
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  - https://dblp.org/rec/journals/corr/abs-2406-01428
Martin J. Hetz, Nicolas Carl, Sarah Haggenmüller, Christoph Wies, Maurice Stephan Michel, Frederik Wessels, Titus J. Brinker:
Superhuman performance in urology board questions by an explainable large language model enabled for context integration of the European Association of Urology guidelines: the UroBot study. CoRR abs/2406.01428 (2024)

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