Risk of Bias and Error From Data Sets Used for Dermatologic Artificial Intelligence
Author:
Affiliation:
1. Department of Dermatology, Medical University of Vienna, Vienna, Austria
Publisher
American Medical Association (AMA)
Subject
Dermatology
Link
https://jamanetwork.com/journals/jamadermatology/articlepdf/2784298/jamadermatology_tschandl_2021_ed_210013_1636039148.31264.pdf
Reference15 articles.
1. Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping review. Published online September 22.;Daneshjou;JAMA Dermatol,2021
2. Bias in, bias out: underreporting and underrepresentation of diverse skin types in machine learning research for skin cancer detection: a scoping review.;Guo;J Am Acad Dermatol
3. Melanoma recognition by a deep learning convolutional neural network: performance in different melanoma subtypes and localisations.;Winkler;Eur J Cancer,2020
4. Expert-level diagnosis of nonpigmented skin cancer by combined convolutional neural networks.;Tschandl;JAMA Dermatol,2019
5. Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study.;Tschandl;Lancet Oncol,2019
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