Skin Scan: Cutting-edge AI-Powered Skin Cancer Classification App for Early Diagnosis and Prevention

Maria Sial; Salman Shakeel; Muhammad Asim; Amaad Khalil; Muhammad Abeer Irfan; Atif Jan

Authors

Maria Sial Departement of Computer Systems Engineering University of Engineering and Technology, Peshawar
Salman Shakeel Departement of Computer Systems Engineering University of Engineering and Technology, Peshawar
Muhammad Asim Departement of Computer Systems Engineering University of Engineering and Technology, Peshawar
Amaad Khalil Departement of Computer Systems Engineering University of Engineering and Technology, Peshawar
Muhammad Abeer Irfan Departement of Computer Systems Engineering University of Engineering and Technology, Peshawar
Atif Jan Department of Electrical Engineering University of Engineering and Technology, Peshawar

Keywords:

Skin Cancer, AI-Powered, Skin Lesions, Skin Cancer Types (Basal Cell Carcinoma, Squamous Cell Carcinoma, Melanoma), Image Classification.

Abstract

Mobile health applications (mHealth) use machine learning (AI)-based algorithms to classify skin lesions; nevertheless, the influence on healthcare systems is unknown. In 2019, a large Dutch health insurance provider provided 2.2 million people with free mHealth software for skin cancer screening. To evaluate the effects on dermatological care consumption, the research conducted a practical transitional and population-based study. To evaluate dermatological needs between the two groups throughout the first year of free access, the research compared 18,960 mHealth users who completed at least one successful evaluation with the app to 56,880 controls who did not use the app. The odds ratios (OR) were then computed. A cost-effectiveness analysis was conducted in the near term to find out the expense for each extra-diagnosed premalignancy. Here, results indicate that mHealth users had a three-fold greater incidence of requests for benign tumors on the skin and the nevi (5.9% vs 1.7%, OR 3.7 (95% CI 3.4–4.1)), and they had greater numbers of claims for (pre)malignant skin cancers as groups (6.0% vs 4.6%, OR 1.3 (95% CI 1.2– 1.4)). Compared to the existing standard of care, the expenses associated with using the app to detect one additional (pre) malignant skin lesion were €2567. These results suggest that AI in m Health may help identify more dermatological (pre)malignancies, but this could be weighed against the current greater rise in the need for care for benign tumors of the skin and nevi.

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