Artificial Intelligence in Early Detection of Skin Cancer

Rebout, Frank

doi:10.22034/mphrj.2026.585087.1091

Artificial Intelligence in Early Detection of Skin Cancer

Document Type : Original Article

Author

Frank Rebout

Department of Chemical Engineering, Calgary University, Canada

10.22034/mphrj.2026.585087.1091

Abstract

Skin cancer is one of the most common malignancies worldwide, and its early detection remains essential for improving survival rates and reducing healthcare burdens. Dermoscopy has significantly enhanced the diagnostic accuracy of skin cancer by allowing detailed visualization of subsurface skin structures, but its interpretation depends heavily on clinical expertise and experience. In recent years, artificial intelligence (AI) has emerged as a transformative tool in medical imaging, particularly in dermatology, enabling automated, rapid, and highly accurate analysis of dermoscopic images. This study explores the integration of AI techniques—such as convolutional neural networks (CNNs), deep learning, and ensemble models—for the early detection and classification of skin lesions, including melanoma, basal cell carcinoma, and benign nevi. By leveraging large annotated datasets and advanced feature extraction algorithms, AI-based diagnostic systems can identify subtle visual patterns that may elude human observation. The paper reviews the main architectures applied to dermoscopy, discusses data preprocessing and augmentation strategies, and analyzes the performance of AI compared to dermatologists in clinical studies. Moreover, it highlights explainability, interpretability, and ethical considerations in AI-assisted diagnosis, ensuring transparency and trust in clinical applications. Results from recent research indicate that AI models achieve dermatologist-level accuracy and can serve as valuable decision-support systems, particularly in teledermatology and resource-limited settings. The findings suggest that AI-powered dermoscopic analysis represents a paradigm shift toward personalized, accessible, and efficient skin cancer screening, with significant implications for global public health and digital medicine.

Graphical Abstract

Keywords

Artificial Intelligence

Dermoscopy

Skin Cancer Detection

Deep Learning

Medical Imaging

Subjects

Cancer

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