Deep learning has the potential to automate many clinically useful tasks in medical imaging.
However translation of deep learning into clinical practice has been hindered by issues such …

The development of diagnostic tools for skin cancer based on artificial intelligence (AI) is
increasing rapidly and will likely soon be widely implemented in clinical use. Even though …

Supervised learning for dermatology requires a large volume of annotated images, but
collecting clinical data is costly, and it is virtually impossible to cover all clinical cases …

Skin cancer is a serious condition that requires accurate diagnosis and treatment. One way
to assist clinicians in this task is using computer-aided diagnosis tools that automatically …

The remarkable progress of deep learning in dermatological tasks has brought us closer to
achieving diagnostic accuracies comparable to those of human experts. However, while …

The disparate performance of face analytics technology across demographic groups is a
well-documented phenomenon. In particular, these systems tend toward lower accuracy for …