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 …

Background Multiple studies have compared the performance of artificial intelligence (AI)–
based models for automated skin cancer classification to human experts, thus setting the …

The number of publications on deep learning for cancer diagnostics is rapidly increasing,
and systems are frequently claimed to perform comparable with or better than clinicians …

Publicly available skin image datasets are increasingly used to develop machine learning
algorithms for skin cancer diagnosis. However, the total number of datasets and their …

Abstract The International Skin Imaging Collaboration (ISIC) datasets have become a
leading repository for researchers in machine learning for medical image analysis …

Background Deep learning offers considerable promise for medical diagnostics. We aimed
to evaluate the diagnostic accuracy of deep learning algorithms versus health-care …

Background Recent studies have successfully demonstrated the use of deep-learning
algorithms for dermatologist-level classification of suspicious lesions by the use of excessive …

Artificial intelligence (AI) has wide applications in healthcare, including dermatology.
Machine learning (ML) is a subfield of AI involving statistical models and algorithms that can …

Recently, there has been great interest in developing Artificial Intelligence (AI) enabled
computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing …

Background Melanoma is the most dangerous type of skin cancer but is curable if detected
early. Recent publications demonstrated that artificial intelligence is capable in classifying …