Evaluation of diagnosis diversity in artificial intelligence datasets: a scoping review-Reference-Cited by-同舟云学术

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Evaluation of diagnosis diversity in artificial intelligence datasets: a scoping review

Published:2022-10-23 Issue:2 Volume:188 Page:292-294
ISSN:0007-0963
Container-title:British Journal of Dermatology
language:en
Short-container-title:

Author:

Chen Michael L¹^ORCID,Rotemberg Veronica²,Lester Jenna C³^ORCID,Novoa Roberto A¹⁴,Chiou Albert S¹,Daneshjou Roxana¹

Affiliation:

1. Department of Dermatology and

2. Dermatology Service, Memorial Sloan Kettering Cancer Center , New York, NY , USA

3. Department of Dermatology, University of California San Francisco , San Francisco, CA , USA

4. Department of Pathology, Stanford University School of Medicine , Stanford, CA , USA

Funder

National Institutes of Health

Publisher

Oxford University Press (OUP)

Subject

Dermatology

Link

https://academic.oup.com/bjd/advance-article-pdf/doi/10.1093/bjd/ljac047/48318095/ljac047.pdf

Reference8 articles.

1. Lack of transparency and potential bias in artificial intelligence data sets and algorithms: a scoping review;Daneshjou,2021

2. Characteristics of publicly available skin cancer image datasets: a systematic review;Wen,2022

3. Evaluation of artificial intelligence-assisted diagnosis of skin neoplasms – a single-center, paralleled, unmasked, randomized controlled trial;Han;J Invest Dermatol,2022

4. Dermatologist-level classification of skin cancer with deep neural networks;Esteva;Nature,2017

5. Epidemiology of skin diseases in people of color;Taylor;Cutis,2003

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3. Best Practices for Research in Virtual and Augmented Reality in Dermatology;Journal of Investigative Dermatology;2024-01

4. Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease;npj Digital Medicine;2023-09-27

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