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Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms

Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms Key PointsQuestionHow transparent are the data sets used to develop artificial intelligence (AI) algorithms in dermatology, and what potential pitfalls exist in the data? FindingsIn this scoping review of 70 studies addressing the intersection of dermatology and AI that were published between January 1, 2015, and November 1, 2020, most data set descriptions were inadequate for analysis and replication, disease labels did not meet the gold standard, and information on patient skin tone and race or ethnicity was often not reported. In addition, most data sets and models have not been shared publicly. MeaningThese findings suggest that the applicability and generalizability of AI algorithms rely on high-quality training and testing data sets; the sparsity of data set descriptions, lack of data set and model transparency, inconsistency in disease labels, and lack of reporting on patient diversity present concerns for the clinical translation of these algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA Dermatology American Medical Association

Lack of Transparency and Potential Bias in Artificial Intelligence Data Sets and Algorithms

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Publisher
American Medical Association
Copyright
Copyright 2021 American Medical Association. All Rights Reserved.
ISSN
2168-6068
eISSN
2168-6084
DOI
10.1001/jamadermatol.2021.3129
Publisher site
See Article on Publisher Site

Abstract

Key PointsQuestionHow transparent are the data sets used to develop artificial intelligence (AI) algorithms in dermatology, and what potential pitfalls exist in the data? FindingsIn this scoping review of 70 studies addressing the intersection of dermatology and AI that were published between January 1, 2015, and November 1, 2020, most data set descriptions were inadequate for analysis and replication, disease labels did not meet the gold standard, and information on patient skin tone and race or ethnicity was often not reported. In addition, most data sets and models have not been shared publicly. MeaningThese findings suggest that the applicability and generalizability of AI algorithms rely on high-quality training and testing data sets; the sparsity of data set descriptions, lack of data set and model transparency, inconsistency in disease labels, and lack of reporting on patient diversity present concerns for the clinical translation of these algorithms.

Journal

JAMA DermatologyAmerican Medical Association

Published: Nov 22, 2021

References