European journal of cancer (Oxford, England : 1990)
31419752
BACKGROUND: Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not reflect the clinical reality of skin cancer s...
European journal of cancer (Oxford, England : 1990)
31401469
BACKGROUND: Melanoma is the most dangerous type of skin cancer but is curable if detected early. Recent publications demonstrated that artificial intelligence is capable in classifying images of benign nevi and melanoma with dermatologist-level preci...
European journal of cancer (Oxford, England : 1990)
31518967
BACKGROUND: In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial intelligence were considered as opponents. Howe...
The use of computers or machines in medicine dates back to the 1960s. Deep learning software programming is a subset of artificial intelligence (AI) based on the ability of a machine to learn from data and adaptively change. Deep learning is creating...
British journal of hospital medicine (London, England : 2005)
32003624
The significance of early diagnosis for melanoma prognosis and survival cannot be understated. The public health benefits of melanoma prevention and detection have driven advances in diagnostics for skin cancer, particularly in the field of artificia...
Journal of the European Academy of Dermatology and Venereology : JEADV
31856342
BACKGROUND: Deep learning convolutional neural networks (CNN) may assist physicians in the diagnosis of melanoma. The capacity of a CNN to differentiate melanomas from combined naevi, the latter representing well-known melanoma simulators, has not be...
Although deep learning algorithms have demonstrated expert-level performance, previous efforts were mostly binary classifications of limited disorders. We trained an algorithm with 220,680 images of 174 disorders and validated it using Edinburgh (1,3...
Journal of the American Academy of Dermatology
32434009
Artificial intelligence is generating substantial interest in the field of medicine. One form of artificial intelligence, deep learning, has led to rapid advances in automated image analysis. In 2017, an algorithm demonstrated the ability to diagnose...
Journal of the American Academy of Dermatology
32428608
Because of a convergence of the availability of large data sets, graphics-specific computer hardware, and important theoretical advancements, artificial intelligence has recently contributed to dramatic progress in medicine. One type of artificial in...
Skin conditions affect 1.9 billion people. Because of a shortage of dermatologists, most cases are seen instead by general practitioners with lower diagnostic accuracy. We present a deep learning system (DLS) to provide a differential diagnosis of sk...