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Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts - PMC

ncbi.nlm.nih.gov · 11,197 words · saved by 1 readers

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site. The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely. Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now. 1Department of Electrical Engineering, Stanford University, Stanford, CA, USA. 2Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA. 2Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA. 3Department of Computer Science, Stanford University, Stanford, CA, USA. 1Department of Electrical Engineering, Stanford University, Stanford, CA, USA. 2Stanford Center for Artificial Intelligence in M

Res Sq [Preprint]. 2023 Oct 30:rs.3.rs-3483777. [Version 1] doi: 10.21203/rs.3.rs-3483777/v1 Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts Dave Van Veen Dave Van Veen 1 Department of Electrical Engineering, Stanford University, Stanford, CA, USA. 2 Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA. Find articles by Dave Van Veen 1, 2, † , Cara Van Uden Cara Van Uden 2 Stanford Center for Artificial Intelligence in Medicine and Imaging, Palo Alto, CA, USA. 3 Department of Computer Science, Stanford University, Stan

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