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Review
. 2020 Jun;10(3):365-386.
doi: 10.1007/s13555-020-00372-0. Epub 2020 Apr 6.

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Stephanie Chan  1 , Vidhatha Reddy  1 , Bridget Myers  1 , Quinn Thibodeaux  1 , Nicholas Brownstone  1 , Wilson Liao  2
Affiliations
Review

Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations

Stephanie Chan et al. Dermatol Ther (Heidelb). 2020 Jun.

Abstract

Machine learning (ML) has the potential to improve the dermatologist's practice from diagnosis to personalized treatment. Recent advancements in access to large datasets (e.g., electronic medical records, image databases, omics), faster computing, and cheaper data storage have encouraged the development of ML algorithms with human-like intelligence in dermatology. This article is an overview of the basics of ML, current applications of ML, and potential limitations and considerations for further development of ML. We have identified five current areas of applications for ML in dermatology: (1) disease classification using clinical images; (2) disease classification using dermatopathology images; (3) assessment of skin diseases using mobile applications and personal monitoring devices; (4) facilitating large-scale epidemiology research; and (5) precision medicine. The purpose of this review is to provide a guide for dermatologists to help demystify the fundamentals of ML and its wide range of applications in order to better evaluate its potential opportunities and challenges.

Keywords: Artificial intelligence; Convolutional neural network; Deep learning; Dermatology; Image classification; Machine learning; Mobile applications; Personal monitoring devices; Precision medicine.

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Conflict of interest statement

Stephanie Chan, Vidhatha Reddy, Bridget Myers, Quinn Thibodeaux, Nicholas Brownstone have nothing to disclose. Wilson Liao is a member of the journal’s Editorial Board.

Figures

Fig. 1
Fig. 1
Artificial intelligence and machine learning. Machine learning is a type of artificial intelligence. Some common types of machine learning approaches used in dermatology include convolutional neural network (CNN), natural language processing (NLP), support vector machine, and random forest. Notably, there are many other possible machine learning approaches that are not listed and out of the scope of this review
Fig. 2
Fig. 2
Applications of machine learning in dermatology. Flowchart demonstrating the various sources of data in dermatology, machine learning models, and potential applications. Icons were created with the web-based program BioRender (https://biorender.com)
Fig. 3
Fig. 3
Limitations of machine learning. Icons were created with the web-based program BioRender (https://biorender.com)
See this image and copyright information in PMC

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