AIMC Topic: Dermatologists

Clear Filters Showing 51 to 58 of 58 articles

The Importance of Incorporating Human Factors in the Design and Implementation of Artificial Intelligence for Skin Cancer Diagnosis in the Real World.

American journal of clinical dermatology
Artificial intelligence (AI) algorithms have been shown to diagnose skin lesions with impressive accuracy in experimental settings. The majority of the literature to date has compared AI and dermatologists as opponents in skin cancer diagnosis. Howev...

Using deep learning for dermatologist-level detection of suspicious pigmented skin lesions from wide-field images.

Science translational medicine
A reported 96,480 people were diagnosed with melanoma in the United States in 2019, leading to 7230 reported deaths. Early-stage identification of suspicious pigmented lesions (SPLs) in primary care settings can lead to improved melanoma prognosis an...

Deep learning-based, computer-aided classifier developed with dermoscopic images shows comparable performance to 164 dermatologists in cutaneous disease diagnosis in the Chinese population.

Chinese medical journal
BACKGROUND: Diagnoses of Skin diseases are frequently delayed in China due to lack of dermatologists. A deep learning-based diagnosis supporting system can facilitate pre-screening patients to prioritize dermatologists' efforts. We aimed to evaluate ...

Is artificial intelligence going to replace dermatologists?

Cutis
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...

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

Annals of oncology : official journal of the European Society for Medical Oncology
BACKGROUND: Deep learning convolutional neural networks (CNN) may facilitate melanoma detection, but data comparing a CNN's diagnostic performance to larger groups of dermatologists are lacking.