Machine learning for precision dermatology: Advances, opportunities, and outlook

J Am Acad Dermatol. 2021 May;84(5):1458-1459. doi: 10.1016/j.jaad.2020.06.1019. Epub 2020 Jul 6.

Authors

Ernest Y Lee¹, Nolan J Maloney², Kyle Cheng², Daniel Q Bach²

Affiliations

¹ Department of Bioengineering, University of California-Los Angeles; Division of Dermatology, Department of Medicine, University of California-Los Angeles; University of California-Los Angeles-Caltech Medical Scientist Training Program, David Geffen School of Medicine at University of California-Los Angeles. Electronic address: ernest.lee@ucla.edu.
² Division of Dermatology, Department of Medicine, University of California-Los Angeles.

No abstract available

Publication types

Letter
Review

MeSH terms

Big Data
Dermatology / methods*
Gene Expression Profiling / methods
Genomics / methods
Humans
Image Processing, Computer-Assisted / methods
Machine Learning*
Photography
Precision Medicine / methods*
Skin / diagnostic imaging
Skin Diseases / diagnosis*
Skin Diseases / genetics
Skin Diseases / therapy

Grants and funding