Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms
This scoping review identified 3 issues in data sets used to develop and test clinical AI algorithms for skin disease that should be addressed before clinical translation: sparsity of data set characterization and lack of transparency, nonstandard and unverified disease labels, and inability to fully assess patient diversity used for algorithm development and testing.
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.