Large language models encode clinical knowledge

Large language models (LLMs) have demonstrated impressive capabilities, but the bar for clinical applications is high. Attempts to assess the clinical knowledge of models typically rely on automated evaluations based on limited benchmarks. Here, to address these limitations, we present MultiMedQA, a...

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Detalles Bibliográficos
Autores principales: Singhal, Karan, Azizi, Shekoofeh, Tu, Tao, Mahdavi, S. Sara, Wei, Jason, Chung, Hyung Won, Scales, Nathan, Tanwani, Ajay, Cole-Lewis, Heather, Pfohl, Stephen, Payne, Perry, Seneviratne, Martin, Gamble, Paul, Kelly, Chris, Babiker, Abubakr, Schärli, Nathanael, Chowdhery, Aakanksha, Mansfield, Philip, Demner-Fushman, Dina, Agüera y Arcas, Blaise, Webster, Dale, Corrado, Greg S., Matias, Yossi, Chou, Katherine, Gottweis, Juraj, Tomasev, Nenad, Liu, Yun, Rajkomar, Alvin, Barral, Joelle, Semturs, Christopher, Karthikesalingam, Alan, Natarajan, Vivek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10396962/
https://www.ncbi.nlm.nih.gov/pubmed/37438534
http://dx.doi.org/10.1038/s41586-023-06291-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10396962/
https://www.ncbi.nlm.nih.gov/pubmed/37438534
http://dx.doi.org/10.1038/s41586-023-06291-2

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