Machine Learning for Health: Algorithm Auditing & Quality Control

Developers proposing new machine learning for health (ML4H) tools often pledge to match or even surpass the performance of existing tools, yet the reality is usually more complicated. Reliable deployment of ML4H to the real world is challenging as examples from diabetic retinopathy or Covid-19 scree...

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Detalles Bibliográficos
Autores principales: Oala, Luis, Murchison, Andrew G., Balachandran, Pradeep, Choudhary, Shruti, Fehr, Jana, Leite, Alixandro Werneck, Goldschmidt, Peter G., Johner, Christian, Schörverth, Elora D. M., Nakasi, Rose, Meyer, Martin, Cabitza, Federico, Baird, Pat, Prabhu, Carolin, Weicken, Eva, Liu, Xiaoxuan, Wenzel, Markus, Vogler, Steffen, Akogo, Darlington, Alsalamah, Shada, Kazim, Emre, Koshiyama, Adriano, Piechottka, Sven, Macpherson, Sheena, Shadforth, Ian, Geierhofer, Regina, Matek, Christian, Krois, Joachim, Sanguinetti, Bruno, Arentz, Matthew, Bielik, Pavol, Calderon-Ramirez, Saul, Abbood, Auss, Langer, Nicolas, Haufe, Stefan, Kherif, Ferath, Pujari, Sameer, Samek, Wojciech, Wiegand, Thomas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2021
Materias:
Implementation Science & Operations Management
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562935/
https://www.ncbi.nlm.nih.gov/pubmed/34729675
http://dx.doi.org/10.1007/s10916-021-01783-y

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562935/
https://www.ncbi.nlm.nih.gov/pubmed/34729675
http://dx.doi.org/10.1007/s10916-021-01783-y

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