Cargando…
Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model
As artificial intelligence (AI) makes continuous progress to improve quality of care for some patients by leveraging ever increasing amounts of digital health data, others are left behind. Empirical evaluation studies are required to keep biased AI models from reinforcing systemic health disparities...
Autores principales: | Röösli, Eliane, Bozkurt, Selen, Hernandez-Boussard, Tina |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8786878/ https://www.ncbi.nlm.nih.gov/pubmed/35075160 http://dx.doi.org/10.1038/s41597-021-01110-7 |
Ejemplares similares
-
Bias at warp speed: how AI may contribute to the disparities gap in the time of COVID-19
por: Röösli, Eliane, et al.
Publicado: (2020) -
Fairness and generalizability of OCT normative databases: a comparative analysis
por: Nakayama, Luis Filipe, et al.
Publicado: (2023) -
The hidden half comes into the spotlight: Peeking inside the black box of root developmental phases
por: Siqueira, João Antonio, et al.
Publicado: (2021) -
A Peek into Pandora’s Box: COVID-19 and Neurodegeneration
por: Chandra, Abhishek, et al.
Publicado: (2022) -
Peeking Inside the Statistical Black Box: How to Analyze Quantitative Information and Get It Right the First Time
por: Fairman, Kathleen A.
Publicado: (2007)