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

Lee, Ernest Y. and Maloney, Nolan J. and Cheng, Kyle and Bach, Daniel Q. (2021) Machine learning for precision dermatology: Advances, opportunities, and outlook. Journal of the American Academy of Dermatology, 84 (5). pp. 1458-1459. ISSN 0190-9622. PMCID PMC8023050. doi:10.1016/j.jaad.2020.06.1019. https://resolver.caltech.edu/CaltechAUTHORS:20200707-113031872

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Abstract

To the Editor: With the explosion of big data in medicine driven by the advent of electronic medical records, next-generation sequencing, multi-omics, and noninvasive imaging techniques, dermatology is a field at the precipice of an artificial intelligence (AI) revolution. However, to the majority of clinicians, machine learning (ML) is a magical black box that is powerful but inaccessible. Here, we review the latest advances in ML applied to dermatologic diagnosis and treatment and highlight key discoveries with translational potential. ML is an AI technique that focuses on designing machines (or computers) that mimic human pattern recognition and problem solving.1 With the rise of big data and data science, ML and AI already affect our daily lives in innumerable ways. Comparatively, clinical medicine has been slower to integrate ML into daily practice.2 ML has typically been considered a tool well outside of a typical clinician's purview. At the same time, there is now an enormous demand for high-quality research that is advancing health care using ML and AI.3 ML is a natural fit for translation into dermatology because dermatology is a specialty that is heavily reliant on visual evaluation and pattern recognition.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.jaad.2020.06.1019DOIArticle
ORCID:
AuthorORCID
Lee, Ernest Y.0000-0001-5144-2552
Maloney, Nolan J.0000-0002-9722-5673
Additional Information:© 2020 by the American Academy of Dermatology, Inc. Received 12 September 2019, Revised 8 June 2020, Accepted 26 June 2020, Available online 6 July 2020. 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.
Funders:
Funding AgencyGrant Number
UCLA-Caltech Medical Scientist Training ProgramUNSPECIFIED
NIH Predoctoral FellowshipT32GM008042
NIH Predoctoral FellowshipT32AR071307
National Psoriasis FoundationUNSPECIFIED
Subject Keywords:Machine learning; artificial intelligence; precision medicine; precision dermatology; biomarkers; diagnosis; treatment
Issue or Number:5
PubMed Central ID:PMC8023050
DOI:10.1016/j.jaad.2020.06.1019
Record Number:CaltechAUTHORS:20200707-113031872
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200707-113031872
Official Citation:Ernest Y. Lee, Nolan J. Maloney, Kyle Cheng, Daniel Q. Bach, Machine learning for precision dermatology: Advances, opportunities, and outlook, Journal of the American Academy of Dermatology, Volume 84, Issue 5, 2021, Pages 1458-1459, ISSN 0190-9622, https://doi.org/10.1016/j.jaad.2020.06.1019. (https://www.sciencedirect.com/science/article/pii/S0190962220321617)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:104251
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Jul 2020 18:40
Last Modified:23 Apr 2021 16:47

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