Artificial intelligence (AI) is transforming skin cancer screening and diagnosis, offering both disruptive and assistive capabilities. This review explores current AI applications in dermatology, focusing on image and molecular processing for skin cancer detection, patient self-screening, and improving diagnostic accuracy for non-dermatologists. It also discusses challenges in clinical implementation, including algorithmic bias, data quality, and the need for robust validation. AI models can analyze images, integrate clinical data, and identify multiple lesions from wide-field images. However, they face limitations in real-world performance, data variability, and generalizability across different skin types. AI has shown promise in assisting primary care providers, dermatologists, and dermatopathologists, with applications in predicting skin cancer types, diagnosing melanoma, and improving treatment decisions. AI can also enhance dermatopathology by analyzing whole slide images and supporting clinical decisions. Despite these advancements, AI models require further refinement to ensure accuracy, fairness, and clinical readiness. The integration of AI into dermatology is ongoing, with efforts to improve image quality, reduce biases, and ensure transparency in model validation. Future research focuses on federated learning, uncertainty estimation, multimodal learning, and emerging model architectures like vision transformers. AI has the potential to democratize skin cancer screening, improve access to care, and enhance diagnostic accuracy, but its implementation requires careful consideration of ethical, technical, and clinical factors.Artificial intelligence (AI) is transforming skin cancer screening and diagnosis, offering both disruptive and assistive capabilities. This review explores current AI applications in dermatology, focusing on image and molecular processing for skin cancer detection, patient self-screening, and improving diagnostic accuracy for non-dermatologists. It also discusses challenges in clinical implementation, including algorithmic bias, data quality, and the need for robust validation. AI models can analyze images, integrate clinical data, and identify multiple lesions from wide-field images. However, they face limitations in real-world performance, data variability, and generalizability across different skin types. AI has shown promise in assisting primary care providers, dermatologists, and dermatopathologists, with applications in predicting skin cancer types, diagnosing melanoma, and improving treatment decisions. AI can also enhance dermatopathology by analyzing whole slide images and supporting clinical decisions. Despite these advancements, AI models require further refinement to ensure accuracy, fairness, and clinical readiness. The integration of AI into dermatology is ongoing, with efforts to improve image quality, reduce biases, and ensure transparency in model validation. Future research focuses on federated learning, uncertainty estimation, multimodal learning, and emerging model architectures like vision transformers. AI has the potential to democratize skin cancer screening, improve access to care, and enhance diagnostic accuracy, but its implementation requires careful consideration of ethical, technical, and clinical factors.