Clinical utility of a digital dermoscopy image-based artificial intelligence device in the diagnosis and management of skin cancer by dermatologists Journal Article


Authors: Witkowski, A. M.; Burshtein, J.; Christopher, M.; Cockerell, C.; Correa, L.; Cotter, D.; Ellis, D. L.; Farberg, A. S.; Grant-Kels, J. M.; Greiling, T. M.; Grichnik, J. M.; Leachman, S. A.; Linfante, A.; Marghoob, A.; Marks, E.; Nguyen, K.; Ortega-Loayza, A. G.; Paragh, G.; Pellacani, G.; Rabinovitz, H.; Rigel, D.; Siegel, D. M.; Song, E. J.; Swanson, D.; Trask, D.; Ludzik, J.
Article Title: Clinical utility of a digital dermoscopy image-based artificial intelligence device in the diagnosis and management of skin cancer by dermatologists
Abstract: 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. © 2024 by the authors.
Keywords: adult; controlled study; squamous cell carcinoma; cancer diagnosis; sensitivity and specificity; accuracy; melanoma; dermoscopy; basal cell carcinoma; skin defect; prevalence; skin cancer; questionnaire; artificial intelligence; dermatoscopy; dermatologist; diagnostic test accuracy study; demographics; machine learning; atypical nevi; human; male; female; article; convolutional neural network
Journal Title: Cancers
Volume: 16
Issue: 21
ISSN: 2072-6694
Publisher: MDPI  
Date Published: 2024-11-01
Start Page: 3592
Language: English
DOI: 10.3390/cancers16213592
PROVIDER: scopus
PMCID: PMC11545296
PUBMED: 39518033
DOI/URL:
Notes: Article -- Source: Scopus
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  1. Ashfaq A Marghoob
    534 Marghoob