AIMC Topic: Dermatologists
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Dermatologist versus artificial intelligence confidence in dermoscopy diagnosis: Complementary information that may affect decision-making.
Experimental dermatology
Aug 3, 2023
In dermatology, deep learning may be applied for skin lesion classification. However, for a given input image, a neural network only outputs a label, obtained using the class probabilities, which do not model uncertainty. Our group developed a novel ...
Artificial Intelligence Training in Canadian Dermatology to Increase Dermatologists Engagement and Enhance Medical Practice.
Journal of cutaneous medicine and surgery
Nov 30, 2022
Evaluation of Melanoma Thickness with Clinical Close-up and Dermoscopic Images Using a Convolutional Neural Network.
Acta dermato-venereologica
Oct 11, 2022
Convolutional neural networks (CNNs) have shown promise in discriminating between invasive and in situ melanomas. The aim of this study was to analyse how a CNN model, integrating both clinical close-up and dermoscopic images, performed compared with...
COVID-19 and artificial intelligence: Experts and dermatologists perspective.
Journal of cosmetic dermatology
Aug 31, 2022
INTRODUCTION: Artificial intelligence (AI) has an important role to play in future healthcare offerings. Machine learning and artificial neural networks are subsets of AI that refer to the incorporation of human intelligence into computers to think a...
The introduction of "Dr AI": What dermatologists should consider.
Journal of the American Academy of Dermatology
Jan 22, 2022
Skin cancer classification via convolutional neural networks: systematic review of studies involving human experts.
European journal of cancer (Oxford, England : 1990)
Sep 8, 2021
BACKGROUND: Multiple studies have compared the performance of artificial intelligence (AI)-based models for automated skin cancer classification to human experts, thus setting the cornerstone for a successful translation of AI-based tools into clinic...
AI outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-CNN architecture with custom mini-batch logic and loss function.
Scientific reports
Sep 1, 2021
Melanoma, one of the most dangerous types of skin cancer, results in a very high mortality rate. Early detection and resection are two key points for a successful cure. Recent researches have used artificial intelligence to classify melanoma and nevu...
Development of a light-weight deep learning model for cloud applications and remote diagnosis of skin cancers.
The Journal of dermatology
Nov 19, 2020
Skin cancer is among the 10 most common cancers. Recent research revealed the superiority of artificial intelligence (AI) over dermatologists to diagnose skin cancer from predesignated and cropped images. However, there remain several uncertainties f...
Dermoscopic diagnostic performance of Japanese dermatologists for skin tumors differs by patient origin: A deep learning convolutional neural network closes the gap.
The Journal of dermatology
Oct 15, 2020
In the dermoscopic diagnosis of skin tumors, it remains unclear whether a deep neural network (DNN) trained with images from fair-skinned-predominant archives is helpful when applied for patients with darker skin. This study compared the performance ...
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