The practice of dermoscopy has evolved significantly over the past century.¹ Dermatologists use dermoscopes to visualize skin findings and improve diagnostic accuracy; this practice has steadily grown to be widely accepted. Dermoscopy courses, residency training, algorithms, atlases, working groups, and other academic resources are readily available. There is a broad selection of dermoscopes on the market, each with distinct technical specifications. However, it is uncommon to find the technical specifications, such as spectral power distribution (SPD), color rendering index (CRI), or even Kelvin temperature, made available to users of dermoscopic devices, although this information is accessible for lighting in other sectors.

Background: Patients with skin lesions suspicious for skin cancer or atypical melanocytic nevi of uncertain malignant potential often present to dermatologists, who may have variable dermoscopy triage clinical experience. Objective: To evaluate the clinical utility of a digital dermoscopy image-based artificial intelligence algorithm (DDI-AI device) on the diagnosis and management of skin cancers by dermatologists. Methods: Thirty-six United States board-certified dermatologists evaluated 50 clinical images and 50 digital dermoscopy images of the same skin lesions (25 malignant and 25 benign), first without and then with knowledge of the DDI-AI device output. Participants indicated whether they thought the lesion was likely benign (unremarkable) or malignant (suspicious). Results: The management sensitivity of dermatologists using the DDI-AI device was 91.1%, compared to 84.3% with DDI, and 70.0% with clinical images. The management specificity was 71.0%, compared to 68.4% and 64.9%, respectively. The diagnostic sensitivity of dermatologists using the DDI-AI device was 86.1%, compared to 78.8% with DDI, and 63.4% with clinical images. Diagnostic specificity using the DDI-AI device increased to 80.7%, compared to 75.9% and 73.6%, respectively. Conclusion: The use of the DDI-AI device may quickly, safely, and effectively improve dermoscopy performance, skin cancer diagnosis, and management when used by dermatologists, independent of training and experience.

The mango tree (Mangifera indica) produces nutrient-dense fruit that is consumed across the world. Interestingly, despite widespread consumption of its fruit and its categorization in the Anacardiaceae family, allergic reactions to the mango tree and its components are comparatively rare, sometimes manifesting as allergic contact dermatitis (ACD). Evaluation of ACD most commonly includes a thorough clinical assessment with diagnostic support from patch testing and histopathologic review following skin biopsy. In recent years, reflectance confocal microscopy (RCM) has shown promising potential to join the repertoire of diagnostic tools for ACD by enabling dynamic and high-resolution imaging of contact dermatitis in vivo. Herein, we present a unique case of mango sap-induced ACD imaged and diagnosed in real time via RCM.