Background: Mobile health (mHealth) consumer applications (apps) have been integrated
with deep learning for skin cancer risk assessments. However, prospective validation of …

Simple Summary Early detection and resection of cutaneous melanoma are crucial for a
good prognosis. However, visual distinction of early melanomas from benign nevi remains …

Artificial intelligence (AI) based algorithms for classification of suspicious skin lesions have
been implemented in mobile phone apps (mHealth), but their effect on healthcare systems is …

Mobile health skin cancer risk assessment campaign using artificial intelligence on a
population-wide scale: a retrospective cohort analysis GLO: 3NZ/01nov21: jdv17442-fig …

Background and objectives The incidence of skin cancer is rising worldwide and there is
medical need to optimize its early detection. This study was conducted to determine the …

Background Mobile health (mHealth) applications (apps) incorporating artificial intelligence
for skin cancer screening are increasingly reimbursed by health insurers. However, an in …

Skin cancer is a growing public health problem. Early and accurate detection is important,
since prognosis and cost of treatment are highly dependent on cancer stage at detection …

Introduction Deep Ensemble for Recognition of Malignancy (DERM) is an artificial
intelligence as a medical device (AIaMD) tool for skin lesion assessment. Methods We report …

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 …

Background As the use of smartphones continues to surge globally, mobile applications
(apps) have become a powerful tool for healthcare engagement. Prominent among these …