The Accuracy of Skin Cancer Detection Rates with the Implementation of Dermoscopy Among Dermatology Clinicians: A...

The goal is to determine if the implementation of dermoscopy improves the accuracy, specificity, and sensitivity rates of skin cancer detection among dermatology clinicians and identify the optimal training method for dermatology clinicians to become proficient in dermoscopy.

A comprehensive search through the A.T. Still Memorial Library, including the electronic health databases PubMed, Scopus, UpToDate, and CINAHL, was performed. Google Scholar search results were sorted by relevance, and the first 30 pages were included within the search due to the large quantity of results. The search keywords included "skin cancer diagnosis," "accuracy," "detection," "dermoscopy," and "dermatologists." The search was performed in July 2023. The date limitations used within the search parameters ranged from 2017 to 2023 to review the past seven years of publications. The search evaluated reference lists and encompassed those that met the inclusion and exclusion criteria. Dermatologists, dermatology physician assistants, dermatology nurse practitioners, and primary care practitioners were eligible for inclusion. The search included literature from any country. The English language was the only language permitted within the search. Gray literature was included in the search using news, press release, and MedRxiv.

A total of 28 articles met the inclusion criteria. All of the articles included were from peer-reviewed sources and in the English language. The articles came from 10 different countries of origin and were published from 2017 to 2023. The main results of the scoping review discovered that the use of dermoscopy improves the accuracy of skin cancer diagnosis. The results also demonstrated that dermoscopy training is highly variable; multiple different types of diagnostic algorithms are used in the professional medical education systems of the 10 countries included within the scoping review. The dermoscopy training algorithms recommended include pattern analysis, 7-point checklist, Menzies method, Triage Amalgamated Dermoscopy Algorithm, Australasian College of Dermatology Dermoscopy Course, 3-point checklist, ABCD rule, Skin Imaging College of China, and no particular algorithm. Of these, the three most commonly recommended included the 7-point checklist, Menzies method, and pattern analysis.

The results demonstrated that dermoscopy improves the accuracy of skin cancer diagnosis for dermatology clinicians and primary care providers. Key implications of these findings for practice include earlier skin cancer detection, which can lead to reduced rates of morbidity and mortality, reduced overall healthcare costs, reduced number of benign lesions biopsied, and improved patient outcomes.