Presenting machine learning model information to clinical end users with model facts labels

There is tremendous enthusiasm surrounding the potential for machine learning to improve medical prognosis and diagnosis. However, there are risks to translating a machine learning model into clinical care and clinical end users are often unaware of the potential harm to patients. This perspective p...

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Detalles Bibliográficos
Autores principales: Sendak, Mark P., Gao, Michael, Brajer, Nathan, Balu, Suresh
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2020
Materias:
Comment
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7090057/
https://www.ncbi.nlm.nih.gov/pubmed/32219182
http://dx.doi.org/10.1038/s41746-020-0253-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7090057/
https://www.ncbi.nlm.nih.gov/pubmed/32219182
http://dx.doi.org/10.1038/s41746-020-0253-3

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