Cargando…
Racial underrepresentation in dermatological datasets leads to biased machine learning models and inequitable healthcare
OBJECTIVE: Clinical applications of machine learning are promising as a tool to improve patient outcomes through assisting diagnoses, treatment, and analyzing risk factors for screening. Possible clinical applications are especially prominent in dermatology as many diseases and conditions present vi...
Autores principales: | Kleinberg, Giona, Diaz, Michael J, Batchu, Sai, Lucke-Wold, Brandon |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9815490/ https://www.ncbi.nlm.nih.gov/pubmed/36619609 |
Ejemplares similares
-
Omics analysis of uveal melanoma: Leukocyte gene signatures reveal novel survival distinctions and indicate a prognostic role for cytolytic activity scoring
por: Diaz, Michael Joseph, et al.
Publicado: (2022) -
Not all biases are bad: equitable and inequitable biases in machine learning and radiology
por: Pot, Mirjam, et al.
Publicado: (2021) -
Evidence-Based Utility of Adjunct Antioxidant Supplementation for the Prevention and Treatment of Dermatologic Diseases: A Comprehensive Systematic Review
por: Tran, Jasmine Thuy, et al.
Publicado: (2023) -
The underrepresentation of “COVID toes” in skin of color: An example of racial bias or evidence of a tenuous disease association?
por: Cline, Abigail, et al.
Publicado: (2021) -
Membranome Similarity between Glioblastoma Multiforme Cell Lines and Primary Tumors
por: Batchu, Sai, et al.
Publicado: (2023)