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 …

Understanding black box models has become paramount as systems based on opaque
Artificial Intelligence (AI) continue to flourish in diverse real-world applications. In response …

We investigated whether human preferences hold the potential to improve diagnostic
artificial intelligence (AI)-based decision support using skin cancer diagnosis as a use case …

Prior skin image datasets have not addressed patient-level information obtained from
multiple skin lesions from the same patient. Though artificial intelligence classification …

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

Skin cancers occur commonly worldwide. The prognosis and disease burden are highly
dependent on the cancer type and disease stage at diagnosis. We systematically reviewed …

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 …

In the past decade, advances in precision oncology have resulted in an increased demand
for predictive assays that enable the selection and stratification of patients for treatment. The …