Introduction Increasing smartphones access has allowed for increasing development and
use of smart phone applications (apps). Mobile health interventions have previously relied�…

Background: Diabetes and related complications are estimated to cost US $727 billion
worldwide annually. Type 1 diabetes, type 2 diabetes, and gestational diabetes are three�…

Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs)
to classify images of melanoma, with accuracies comparable to those achieved by�…

Telemedicine is slowly transforming the way in which healthcare is delivered and has the
potential to improve access to subspecialty expertise, reduce healthcare costs, and improve�…

Objective To examine the validity and findings of studies that examine the accuracy of
algorithm based smartphone applications (“apps”) to assess risk of skin cancer in suspicious�…

As the Internet of Things (IoT) and the Web of Things (WoT) are becoming a reality, their
interconnection with mobile phone computing is increasing. Mobile phone integrated�…

The advances in mobile technologies and applications are driving the transformation in
health services delivery globally. Mobile phone penetration is increasing exponentially in�…

Importance A high proportion of suspicious pigmented skin lesions referred for investigation
are benign. Techniques to improve the accuracy of melanoma diagnoses throughout the�…

Objective: Digital devices are now broadly accessible and have the capacity to measure
aspects of human behavior with high precision and accuracy, in a standardized manner. The�…

Background Machine learning algorithms achieve expert‐level accuracy in skin lesion
classification based on clinical images. However, it is not yet shown whether these�…