Artificial Intelligence for Skin Cancer Detection: Scoping Review

Abstract

Background Skin cancer is the most common cancer type affecting humans. Traditional skin cancer diagnosis methods are costly, require a professional physician, and take time. Hence, to aid in diagnosing skin cancer, artificial intelligence (AI) tools are being used, including shallow and deep machine learning–based methodologies that are trained to detect and classify skin cancer using computer algorithms and deep neural networks. Objective The aim of this study was to identify and group the different types of AI-based technologies used to detect and classify skin cancer. The study also examined the reliability of the selected papers by studying the correlation between the data set size and the number of diagnostic classes with the performance metrics used to evaluate the models. Methods We conducted a systematic search for papers using Institute of Electrical and Electronics Engineers (IEEE) Xplore, Association for Computing Machinery Digital Library (ACM DL), and Ovid MEDLINE databases following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. The studies included in this scoping review had to fulfill several selection criteria: being specifically about skin cancer, detecting or classifying skin cancer, and using AI technologies. Study selection and data extraction were independently conducted by two reviewers. Extracted data were narratively synthesized, where studies were grouped based on the diagnostic AI techniques and their evaluation metrics. Results We retrieved 906 papers from the 3 databases, of which 53 were eligible for this review. Shallow AI-based techniques were used in 14 studies, and deep AI-based techniques were used in 39 studies. The studies used up to 11 evaluation metrics to assess the proposed models, where 39 studies used accuracy as the primary evaluation metric. Overall, studies that used smaller data sets reported higher accuracy. Conclusions This paper examined multiple AI-based skin cancer detection models. However, a direct comparison between methods was hindered by the varied use of different evaluation metrics and image types. Performance scores were affected by factors such as data set size, number of diagnostic classes, and techniques. Hence, the reliability of shallow and deep models with higher accuracy scores was questionable since they were trained and tested on relatively small data sets of a few diagnostic classes.

Artificial Intelligence for the Classification of Pigmented Skin Lesions in Populations with Skin of Color: A Systematic Review

“…Model Performance AI models in this review showed reasonably highperformance classification using both dermoscopic and clinical images in populations with skin of color. Accuracy was greater than 70% for all binary models reviewed, which is similar to models developed using Caucasian datasets [46]. It has previously been suggested that instead of training algorithms on more diverse datasets, it may be beneficial to develop separate algorithms for different populations [7].…”

Efficacy and Safety of the Genistein Nutraceutical Product Containing Vitamin E, Vitamin B3, and Ceramide on Skin Health in Postmenopausal Women: A Randomized, Double-Blind, Placebo-Controlled Clinical Trial

“…Third, we did not examine the benefits of the GEN product on facial skin at the molecular level. Further research into the advantages of the GEN product using cutting-edge molecular testing and/or some more recent technologies, such as artificial intelligence-based methods for assessing facial skin lesions and conditions, may be of interest [ 47 , 48 ].…”

Diagnostics Using Non-Invasive Technologies in Dermatological Oncology

“…Takkidin at al. [ 126 ] found that shallow models, most frequently, were built using a support vector machine (SVM) with maximum accuracy scores of 93–99% for the diagnosis of skin cancer, using relatively small data sets. On the other hand, in the case of deep models, convolutional neural networks (CNN) were the most commonly used method for skin cancer detection, with a maximum accuracy of 94–96% using medium-size data sets [ 126 ].…”