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

Dermatology and Therapy

Quinn Thibodeaux¹  Stephanie Chan¹  Bridget Myers¹  Vidhatha Reddy¹  Nicholas Brownstone¹  Wilson Liao¹ 

[1] Department of Dermatology, University of California San Francisco;

关键词: Artificial intelligence;    Convolutional neural network;    Deep learning;    Dermatology;    Image classification;    Machine learning;   

DOI  :  10.1007/s13555-020-00372-0

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown