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

Authors

Haleigh Stafford
Jane Buell
Elizabeth Chiang
Uma Ramesh
Michael Migden
Priyadharsini Nagarajan
Moran Amit
Dan Yaniv

Publication Date

6-7-2023

Journal

Cancers

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.

Keywords

artificial intelligence, machine learning, deep learning, deep neural networks, non-melanoma skin cancer, basal cell carcinoma, squamous cell carcinoma, skin cancer screening, smartphone applications

Comments

PMID: 37370703