Adapted large language models can outperform medical experts in clinical text summarization
Adapted large language models can outperform medical experts in clinical text summarization
About this item
Full title
Author / Creator
Van Veen, Dave , Van Uden, Cara , Blankemeier, Louis , Delbrouck, Jean-Benoit , Aali, Asad , Bluethgen, Christian , Pareek, Anuj , Polacin, Malgorzata , Reis, Eduardo Pontes , Seehofnerová, Anna , Rohatgi, Nidhi , Hosamani, Poonam , Collins, William , Ahuja, Neera , Langlotz, Curtis P. , Hom, Jason , Gatidis, Sergios , Pauly, John and Chaudhari, Akshay S.
Publisher
New York: Nature Publishing Group US
Journal title
Language
English
Formats
Publication information
Publisher
New York: Nature Publishing Group US
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More information
Scope and Contents
Contents
Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. H...
Alternative Titles
Full title
Adapted large language models can outperform medical experts in clinical text summarization
Authors, Artists and Contributors
Author / Creator
Van Uden, Cara
Blankemeier, Louis
Delbrouck, Jean-Benoit
Aali, Asad
Bluethgen, Christian
Pareek, Anuj
Polacin, Malgorzata
Reis, Eduardo Pontes
Seehofnerová, Anna
Rohatgi, Nidhi
Hosamani, Poonam
Collins, William
Ahuja, Neera
Langlotz, Curtis P.
Hom, Jason
Gatidis, Sergios
Pauly, John
Chaudhari, Akshay S.
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11479659
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_11479659
Other Identifiers
ISSN
1078-8956,1546-170X
E-ISSN
1546-170X
DOI
10.1038/s41591-024-02855-5
