AIMC Topic: Dermatologists

Clear Filters Showing 21 to 30 of 58 articles

AI outperformed every dermatologist in dermoscopic melanoma diagnosis, using an optimized deep-CNN architecture with custom mini-batch logic and loss function.

Scientific reports
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
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
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 ...

Artificial Intelligence and Its Effect on Dermatologists' Accuracy in Dermoscopic Melanoma Image Classification: Web-Based Survey Study.

Journal of medical Internet research
BACKGROUND: Early detection of melanoma can be lifesaving but this remains a challenge. Recent diagnostic studies have revealed the superiority of artificial intelligence (AI) in classifying dermoscopic images of melanoma and nevi, concluding that th...

Deep learning for dermatologists: Part I. Fundamental concepts.

Journal of the American Academy of Dermatology
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...

Deep learning for dermatologists: Part II. Current applications.

Journal of the American Academy of Dermatology
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...