Bridging Diagnostic Gaps with AI: Skin Cancer Screening and Clinical Practice

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Abstract: Skin cancer, encompassing both melanoma and non-melanoma types, represents a significant and escalating global health challenge characterized by high morbidity and mortality. Conventional diagnostic pathways, reliant on visual inspection and histopathology, are constrained by inconsistent clinical expertise and limited accessibility, particularly in underserved regions. The advent of deep learning in dermatology offers a promising avenue to mitigate these gaps. Advanced architectures, including convolutional neural networks (CNNs), vision transformers (ViTs), and hybrid models, have demonstrated diagnostic accuracies that approach and, in some cases, exceed those of experienced dermatologists in controlled settings. Furthermore, multimodal frameworks that integrate dermoscopic images, clinical photographs, and patient metadata enhance diagnostic robustness, while incorporating genomic data signals a shift toward personalized medicine. However, the translation of this potential into reliable clinical practice is impeded by critical, interconnected challenges. Persistent algorithmic Bias, stemming from nonrepresentative training datasets, threatens to exacerbate existing health disparities. A significant gap in rigorous external validation undermines the generalizability of many models, and the superficial nature of some explainability methods limits clinical trust. This perspective synthesizes these technical, clinical, and ethical dimensions, mapping the complex path from algorithm to bedside. Ultimately, the successful integration of artificial intelligence (AI) will augment, not replace, clinical acumen, offering a scalable solution to improve early detection and patient outcomes globally, contingent upon a steadfast commitment to scientific rigour, validation, and health equity.

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