Artificial Intelligence Applications in Dermatology: Where Do We Stand?

被引:76
作者
Gomolin, Arieh [1 ]
Netchiporouk, Elena [1 ]
Gniadecki, Robert [2 ]
Litvinov, Ivan V. [1 ]
机构
[1] McGill Univ, Hlth Ctr, Div Dermatol, Montreal, PQ, Canada
[2] Univ Alberta, Div Dermatol, Edmonton, AB, Canada
关键词
artificial intelligence; barriers; contact allergens; dermatology; melanoma; nevi; psoriasis; machine learning; SKIN-LESIONS; DERMOSCOPIC IMAGES; DIAGNOSTIC SYSTEM; MELANOMA; CLASSIFICATION; SEGMENTATION; NETWORK; COMPUTER; TEXTURE; MODEL;
D O I
10.3389/fmed.2020.00100
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Artificial intelligence (AI) has become a progressively prevalent Research Topic in medicine and is increasingly being applied to dermatology. There is a need to understand this technology's progress to help guide and shape the future for medical care providers and recipients. We reviewed the literature to evaluate the types of publications on the subject, the specific dermatological topics addressed by AI, and the most challenging barriers to its implementation. A substantial number of original articles and commentaries have been published to date and only few detailed reviews exist. Most AI applications focus on differentiating between benign and malignant skin lesions, however; others exist pertaining to ulcers, inflammatory skin diseases, allergen exposure, dermatopathology, and gene expression profiling. Applications commonly analyze and classify images, however, other tools such as risk assessment calculators are becoming increasingly available. Although many applications are technologically feasible, important implementation barriers have been identified including systematic biases, difficulty of standardization, interpretability, and acceptance by physicians and patients alike. This review provides insight into future research needs and possibilities. There is a strong need for clinical investigation in dermatology providing evidence of success overcoming the identified barriers. With these research goals in mind, an appropriate role for AI in dermatology may be achieved in not so distant future.
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页数:7
相关论文
共 94 条
[1]   A Cloud-Based Infrastructure for Feedback-Driven Training and Image Recognition [J].
Abedini, Mani ;
von Cavallar, Stefan ;
Chakravorty, Rajib ;
Davis, Matthew ;
Garnavi, Rahil .
MEDINFO 2015: EHEALTH-ENABLED HEALTH, 2015, 216 :691-695
[2]  
Afifi S, 2017, IEEE ENG MED BIO, P270, DOI 10.1109/EMBC.2017.8036814
[3]   PREDICTING PRESSURE INJURY IN CRITICAL CARE PATIENTS: A MACHIN E-LEARNING MODEL [J].
Alderden, Jenny ;
Pepper, Ginette Alyce ;
Wilson, Andrew ;
Whitney, Joanne D. ;
Richardson, Stephanie ;
Butcher, Ryan ;
Jo, Yeonjung ;
Cummins, Mollie Rebecca .
AMERICAN JOURNAL OF CRITICAL CARE, 2018, 27 (06) :461-468
[4]  
Cruz-Roa AA, 2013, LECT NOTES COMPUT SC, V8150, P403, DOI 10.1007/978-3-642-40763-5_50
[5]   An unsupervised feature learning framework for basal cell carcinoma image analysis [J].
Arevalo, John ;
Cruz-Roa, Angel ;
Arias, Viviana ;
Romero, Eduardo ;
Gonzalez, Fabio A. .
ARTIFICIAL INTELLIGENCE IN MEDICINE, 2015, 64 (02) :131-145
[6]   Big Data and Machine Learning in Health Care [J].
Beam, Andrew L. ;
Kohane, Isaac S. .
JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION, 2018, 319 (13) :1317-1318
[7]   Dermoscopic Image Segmentation via Multistage Fully Convolutional Networks [J].
Bi, Lei ;
Kim, Jinman ;
Ahn, Euijoon ;
Kumar, Ashnil ;
Fulham, Michael ;
Feng, Dagan .
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 2017, 64 (09) :2065-2074
[8]   Automated diagnosis of myositis from muscle ultrasound: Exploring the use of machine learning and deep learning methods [J].
Burlina, Philippe ;
Billings, Seth ;
Joshi, Neil ;
Albayda, Jemima .
PLOS ONE, 2017, 12 (08)
[9]   Computer-Aided Diagnosis of Skin Lesions Using Conventional Digital Photography: A Reliability and Feasibility Study [J].
Chang, Wen-Yu ;
Huang, Adam ;
Yang, Chung-Yi ;
Lee, Chien-Hung ;
Chen, Yin-Chun ;
Wu, Tian-Yau ;
Chen, Gwo-Shing .
PLOS ONE, 2013, 8 (11)
[10]   Analysis of clinical and dermoscopic features for basal cell carcinoma neural network classification [J].
Cheng, Beibei ;
Stanley, R. Joe ;
Stoecker, William V. ;
Stricklin, Sherea M. ;
Hinton, Kristen A. ;
Nguyen, Thanh K. ;
Rader, Ryan K. ;
Rabinovitz, Harold S. ;
Oliviero, Margaret ;
Moss, Randy H. .
SKIN RESEARCH AND TECHNOLOGY, 2013, 19 (01) :E217-E222