Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms
346
Zitationen
5
Autoren
2021
Jahr
Abstract
This scoping review identified 3 issues in data sets that are used to develop and test clinical AI algorithms for skin disease that should be addressed before clinical translation: (1) sparsity of data set characterization and lack of transparency, (2) nonstandard and unverified disease labels, and (3) inability to fully assess patient diversity used for algorithm development and testing.
Ähnliche Arbeiten
Dermatologist-level classification of skin cancer with deep neural networks
2017 · 13.384 Zit.
Tumor Angiogenesis: Therapeutic Implications
1971 · 10.103 Zit.
Improved Survival with Vemurafenib in Melanoma with BRAF V600E Mutation
2011 · 7.661 Zit.
Final Version of 2009 AJCC Melanoma Staging and Classification
2009 · 4.558 Zit.
Technical Details of Intraoperative Lymphatic Mapping for Early Stage Melanoma
1992 · 4.401 Zit.