Non-Melanoma Skin Cancer Detection in the Age of Advanced Technology: A Review

Abstract

Skin cancer is the most common cancer diagnosis in the United States, with approximately one in five Americans expected to be diagnosed within their lifetime. Non-melanoma skin cancer is the most prevalent type of skin cancer, and as cases rise globally, physicians need reliable tools for early detection. Artificial intelligence has gained substantial interest as a decision support tool in medicine, particularly in image analysis, where deep learning has proven to be an effective tool. Because specialties such as dermatology rely primarily on visual diagnoses, deep learning could have many diagnostic applications, including the diagnosis of skin cancer. Furthermore, with the advancement of mobile smartphones and their increasingly powerful cameras, deep learning technology could also be utilized in remote skin cancer screening applications. Ultimately, the available data for the detection and diagnosis of skin cancer using deep learning technology are promising, revealing sensitivity and specificity that are not inferior to those of trained dermatologists. Work is still needed to increase the clinical use of AI-based tools, but based on the current data and the attitudes of patients and physicians, deep learning technology could be used effectively as a clinical decision-making tool in collaboration with physicians to improve diagnostic efficiency and accuracy.

Two Cases of Pigmented Basal Cell Carcinoma in African American Patients

“…Current estimates are that one in five Americans will develop skin cancer in their lifetime, and cases of nonmelanoma skin cancers are rising [1]. Basal cell carcinoma (BCC) was first described in the year 1827 by Jacob, and it is the most common type of skin cancer [2].…”

Potential of use of modern information technology solutions in the work of hospital infection control team, including antibiotic therapy optimisation, reduction of alert pathogen infections and vaccination popularisation

“…Machine learning (Ml) has already proven its usefulness in medicine, mainly in medical image-based diagnostics, including radiology imaging, like X-ray [13] or computed tomography (Ct) [1], magnetic resonance imaging (MRi) [16] and interpretation of visual images of skin pathologies [17]. new fields of medical applications of these technologies are invented, from expedited and deepened education of medical staff [5], automated pooling data from multiple scientific studies for elevated-level analyses [18], optimisation of drugs and medical equipment supply chain and logistics [19], designing and coordinating take-back programmes for unused medications [20] to making drug supply chains counterfeit-proof [21].…”