Machine learning for precision dermatology: Advances, opportunities, and outlook - 10/04/21
Doi : 10.1016/j.jaad.2020.06.1019
Ernest Y. Lee, MD, PhD a, b, c, ⁎ 
, Nolan J. Maloney, MD b, Kyle Cheng, MD b, Daniel Q. Bach, MD, MPH b
, Nolan J. Maloney, MD b, Kyle Cheng, MD b, Daniel Q. Bach, MD, MPH b a Department of Bioengineering, University of California–Los Angeles
b Division of Dermatology, Department of Medicine, University of California–Los Angeles
c University of California–Los Angeles–Caltech Medical Scientist Training Program, David Geffen School of Medicine at University of California–Los Angeles
∗Reprint requests: Ernest Y. Lee, MD, PhD, David Geffen School of Medicine at University of California-Los Angeles, 10833 Le Conte Ave, Los Angeles, CA 90095University of California–David Geffen School of Medicine at University of California-Los AngelesDermatology10833 Le Conte AveLos AngelesCA90095
| Funding sources: Dr Lee acknowledges support from the University of California–Los Angeles (UCLA)–Caltech Medical Scientist Training Program (T32GM008042), the Dermatology Scientist Training Program (T32AR071307) at UCLA, and an Early Career Research Grant from the National Psoriasis Foundation. |
|
| Conflicts of interest: None disclosed. |
|
| IRB approval status: Not applicable. |
© 2020
American Academy of Dermatology, Inc.. Publié par Elsevier Masson SAS. Tous droits réservés.
Vol 84 - N° 5
P. 1458-1459 - mai 2021 Retour au numéro
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