Transforming Dermatologic Diagnosis: The Role of Artificial Intelligence in Early Skin Cancer Detection

Abstract

Artificial intelligence (AI) has rapidly emerged as a transformative technology in dermatology, offering new possibilities for the early detection of skin cancer. Over the past decade, advances in deep learning, multimodal imaging, and explainable AI have achieved diagnostic performance comparable to, and in some cases exceeding, that of expert dermatologists. This review analyzes scientific evidence published between 2017 and 2025, focusing on diagnostic accuracy, algorithmic diversity, ethical implications, and regional adoption in Latin America, with particular emphasis on Mexico, Colombia, and Ecuador. Using the DMAIC methodological framework (Define–Measure–Analyze–Improve–Control), twenty peer-reviewed studies from high-impact journals such as Nature, The Lancet Digital Health, and npj Digital Medicine were examined through descriptive and comparative analysis. Results demonstrate that AI-based systems exhibit high diagnostic sensitivity (93%), specificity (89%), and accuracy (91%), outperforming dermatologists in controlled settings. Convolutional neural networks (CNNs) remain the dominant architecture, though hybrid, multimodal, and explainable models are gaining clinical relevance. A persistent dataset bias was identified, with light skin tones (I–II) representing 62% of images and darker tones (IV–VI) only 15%, revealing a structural inequity that limits global applicability. Regional initiatives in Mexico, Colombia, and Ecuador are addressing these disparities through the creation of diverse datasets, pilot projects in teledermatology, and ethically guided digital health strategies. The evidence confirms that artificial intelligence, when applied responsibly, has the potential to enhance diagnostic precision, promote healthcare equity, and transform dermatologic practice through human–machine collaboration. AI should not replace clinical expertise but rather expand it—turning early skin cancer detection into a more accurate, inclusive, and globally equitable discipline.

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