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Editorial
. 2021 Nov 17:8:751649.
doi: 10.3389/fmed.2021.751649. eCollection 2021.

Editorial: The Emerging Role of Artificial Intelligence in Dermatology

Farhan Mahmood  1 , Solomon Bendayan  2 , Feras M Ghazawi  3 , Ivan V Litvinov  4
Affiliations
Editorial

Editorial: The Emerging Role of Artificial Intelligence in Dermatology

Farhan Mahmood et al. Front Med (Lausanne). .
No abstract available

Keywords: COVID-19; artificial intelligence; deep learning; dermatology; machine learning; teledermatology.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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  • Editorial on the Research Topic The Emerging Role of Artificial Intelligence in Dermatology

References

    1. Hogarty DT, Mackey DA, Hewitt AW. Current state and future prospects of artificial intelligence in ophthalmology: a review. Clin Exp Ophthalmol. (2019) 47:128–39. 10.1111/ceo.13381 - DOI - PubMed
    1. Vestergaard ME, Menzies SW. Automated diagnostic instruments for cutaneous melanoma. Semin Cutan Med Surg. (2008) 27:32–6. 10.1016/j.sder.2008.01.001 - DOI - PubMed
    1. Gomolin A, Netchiporouk E, Gniadecki R, Litvinov IV. Artificial intelligence applications in dermatology: where do we stand? Front Med. (2020) 7:100. 10.3389/fmed.2020.00100 - DOI - PMC - PubMed
    1. Xie F, Fan H, Li Y, Jiang Z, Meng R, Bovik A. Melanoma classification on dermoscopy images using a neural network ensemble model. IEEE Trans Med Imaging. (2017) 36:849–58. 10.1109/TMI.2016.2633551 - DOI - PubMed
    1. Yu C, Yang S, Kim W, Jung J, Chung KY, Lee SW, Oh B. Acral melanoma detection using a convolutional neural network for dermoscopy images. PLoS ONE. (2018) 13:e0193321. Erratum in: PLoS ONE. (2018) 13:e0196621. 10.1371/journal.pone.0196621 - DOI - PMC - PubMed

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