Development and Assessment of an Artificial Intelligence–Based Tool for Skin Condition Diagnosis by Primary Care Physicians and Nurse Practitioners in Teledermatology Practices

Augmented Intelligence Dermatology: Deep Neural Networks Empower Medical Professionals in Diagnosing Skin Cancer and Predicting Treatment Options for 134 Skin Disorders

(2020) Seung Seog Han et al.   JOURNAL OF INVESTIGATIVE DERMATOLOGY

Teledermatology in the wake of COVID-19: Advantages and challenges to continued care in a time of disarray

(2020) Rohit Gupta et al.   JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY

A deep learning system for differential diagnosis of skin diseases

(2020) Yuan Liu et al.   NATURE MEDICINE

Human–computer collaboration for skin cancer recognition

(2020) Philipp Tschandl et al.   NATURE MEDICINE

Deep learning outperformed 136 of 157 dermatologists in a head-to-head dermoscopic melanoma image classification task

(2019) Titus J. Brinker et al.   EUROPEAN JOURNAL OF CANCER

Comparison of the accuracy of human readers versus machine-learning algorithms for pigmented skin lesion classification: an open, web-based, international, diagnostic study

(2019) Philipp Tschandl et al.   LANCET ONCOLOGY

Systematic outperformance of 112 dermatologists in multiclass skin cancer image classification by convolutional neural networks

(2019) Roman C. Maron et al.   EUROPEAN JOURNAL OF CANCER

Man against machine: diagnostic performance of a deep learning convolutional neural network for dermoscopic melanoma recognition in comparison to 58 dermatologists

(2018) H A Haenssle et al.   ANNALS OF ONCOLOGY

Deep neural networks show an equivalent and often superior performance to dermatologists in onychomycosis diagnosis: Automatic construction of onychomycosis datasets by region-based convolutional deep neural network

(2018) Seung Seog Han et al.   PLoS One

Accuracy of Skin Cancer Diagnosis by Physician Assistants Compared With Dermatologists in a Large Health Care System

(2018) Alyce M. Anderson et al.   JAMA Dermatology

Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017

(2018) Spencer L James et al.   LANCET

Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance

(2017) Yading Yuan et al.   IEEE TRANSACTIONS ON MEDICAL IMAGING

The burden of skin disease in the United States

(2017) Henry W. Lim et al.   JOURNAL OF THE AMERICAN ACADEMY OF DERMATOLOGY

Dermatologist-level classification of skin cancer with deep neural networks

(2017) Andre Esteva et al.   NATURE

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(2016)   BRITISH JOURNAL OF DERMATOLOGY

STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies

(2015) Patrick M Bossuyt et al.   BMJ-British Medical Journal

STARD 2015: an updated list of essential items for reporting diagnostic accuracy studies

(2015) Patrick M Bossuyt et al.   BMJ-British Medical Journal

Evaluating Imaging and Computer-aided Detection and Diagnosis Devices at the FDA

(2012) Brandon D. Gallas et al.   ACADEMIC RADIOLOGY

Outcomes of Referral to Dermatology for Suspicious Lesions

(2011) Kate V. Viola et al.   ARCHIVES OF DERMATOLOGY

Recommendations for research design and reporting in computer-assisted diagnosis to facilitate meta-analysis

(2011) Leila H. Eadie et al.   JOURNAL OF BIOMEDICAL INFORMATICS

Fitzpatrick skin typing: Applications in dermatology

(2009) Silonie Sachdeva   Indian Journal of Dermatology Venereology & Leprology

Skin Diseases in Family Medicine: Prevalence and Health Care Use

(2008) E. W. M. Verhoeven et al.   ANNALS OF FAMILY MEDICINE