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...
Detalles Bibliográficos
| Publicado en: | Journal of Medical Systems
Vol. 45; no. 12; pp. 1 - 9 |
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| 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 |
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| Formato: | editorial
tables/charts
Journal Article
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| Publicado: |
Springer Nature
Dec2021
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| Acceso en línea: | Ver este registro en EBSCOhost
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