NYMC Faculty Publications

Artificial Intelligence in the Non-Invasive Detection of Melanoma

Author Type(s)

Student, Faculty, Resident/Fellow

DOI

10.3390/life14121602

Journal Title

Life

Document Type

Article

Publication Date

12-1-2024

Department

Dermatology

Keywords

algorithms, artificial intelligence, dermoscopy, diagnostic accuracy, melanoma, non-invasive skin imaging, optical coherence tomography, reflectance confocal microscopy, skin cancer, skin cancer detection

Disciplines

Medicine and Health Sciences

Abstract

Skin cancer is one of the most prevalent cancers worldwide, with increasing incidence. Skin cancer is typically classified as melanoma or non-melanoma skin cancer. Although melanoma is less common than basal or squamous cell carcinomas, it is the deadliest form of cancer, with nearly 8300 Americans expected to die from it each year. Biopsies are currently the gold standard in diagnosing melanoma; however, they can be invasive, expensive, and inaccessible to lower-income individuals. Currently, suspicious lesions are triaged with image-based technologies, such as dermoscopy and confocal microscopy. While these techniques are useful, there is wide inter-user variability and minimal training for dermatology residents on how to properly use these devices. The use of artificial intelligence (AI)-based technologies in dermatology has emerged in recent years to assist in the diagnosis of melanoma that may be more accessible to all patients and more accurate than current methods of screening. This review explores the current status of the application of AI-based algorithms in the detection of melanoma, underscoring its potential to aid dermatologists in clinical practice. We specifically focus on AI application in clinical imaging, dermoscopic evaluation, algorithms that can distinguish melanoma from non-melanoma skin cancers, and in vivo skin imaging devices.

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