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

Sifting through vast textual data and summarizing key information from electronic health records (EHR) imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy on a...

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Detalles Bibliográficos
Autores principales: 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, Chaudhari, Akshay S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: American Journal Experts 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635391/
https://www.ncbi.nlm.nih.gov/pubmed/37961377
http://dx.doi.org/10.21203/rs.3.rs-3483777/v1

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10635391/
https://www.ncbi.nlm.nih.gov/pubmed/37961377
http://dx.doi.org/10.21203/rs.3.rs-3483777/v1

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