With an increase in deep learning-based methods, the call for explainability of such methods
grows, especially in high-stakes decision making areas such as medical image analysis …

Artificial Intelligence (AI) has emerged as a useful aid in numerous clinical applications for
diagnosis and treatment decisions. Deep neural networks have shown the same or better …

Importance Clinical artificial intelligence (AI) algorithms have the potential to improve clinical
care, but fair, generalizable algorithms depend on the clinical data on which they are trained …

The rapid increase in telemedicine coupled with recent advances in diagnostic artificial
intelligence (AI) create the imperative to consider the opportunities and risks of inserting AI …

Background Due to their ability to solve complex problems, deep neural networks (DNNs)
are becoming increasingly popular in medical applications. However, decision-making by …

There is often a lack of explanation when artificial intelligence (AI) is used to diagnose skin
lesions, which makes the physician unable to interpret and validate the output; thus …

One of the most prevalent cancers worldwide is skin cancer, and it is becoming more
common as the population ages. As a general rule, the earlier skin cancer can be …

The skin lesion types result in delayed diagnosis due to high similarity in early stages of the
skin cancer. In this regard, deep learning algorithms are well-recognized solutions; however …

A novel deep learning framework is proposed for lesion segmentation and classification.
The proposed technique incorporates two primary phases. For lesion segmentation, Mask …

The recent advancements in artificial intelligence (AI) combined with the extensive amount
of data generated by today's clinical systems, has led to the development of imaging AI …