Artificial Intelligence in preventing and detecting skin cancer. A narrative review

Abstract

Abstract Objectives We present a comprehensive literature review regarding the role of Artificial Intelligence in prevention and detection of skin cancer, emphasizing its diagnostic accuracy, interpretability, and clinical integration while reducing healthcare burden and costs. Methods Research has been conducted across databases such as PubMed and Google Scholar. We selected 38 peer-reviewed studies, relevant to our topic. The synthesis of the articles was based on five research directions. Results Artificial Intelligence particularly trough deep learning and convolutional neural networks, achieved diagnostic accuracies exceeding 90% in differentiating benign from malignant skin lesions using datasets such as International Skin Imaging Collaboration (ISIC) and Human Against Machine with 10,000 training images (HAM10000). Biologically inspired algorithms, residual networks, and autoencoders are all combined in hybrid models to further increase sensitivity and specificity. Our review showcases how effective can Artificial Intelligence be in digital histopathology, automated segmentation, and predictive modeling when implemented correctly. Smartphone applications and prevention campaigns enabled early detection and public awareness of skin cancer. Conclusions Artificial Intelligence will become the ultimate pillar of dermatopathology by cutting down medical related costs and supporting clinicians. Nevertheless, tackling issues regarding dataset diversity, algorithmic clarity, ethical standards, and clinical validation will help us develop transparent, equitable and secure systems that assist rather than replace human verification.