Skin cancer is one of the most common forms of cancer found in human beings around the world and is a major hurdle to health systems due to its high frequency and difficult diagnosis. While early and accurate detection is key for better patient outcomes, traditional methods depend heavily on the clinical expertise of individual physicians. Recent developments in artificial intelligence (AI), especially deep learning (DL) and transfer learning (TL), present new, powerful methods for improving the detection of skin lesions. In this chapter, DL algorithms such as convolutional neural networks (CNNs) were applied, with operations leveled up concerning TL, where pre-trained models are leveraged for accurate diagnosis, along with overcoming important issues like scarcity of annotated datasets and inconsistency in dermoscopic images. This redundancy also hinders the development of model training with small, domain-specific datasets, which is why TL can overcome many common bottlenecks of medical images. We review the incorporation of AI-enabled systems into clinical workflows, the efficacy of deep learning models in the detection of different skin cancer types, and the capacity of such technologies to augment dermatologic serendipity. This chapter offers perspectives on research needs, such as hybrid models combining AI and non-AI data, the integration of ethnic and structural racism in AI systems in healthcare, as well as a need to centralize current AI ethics literature in the healthcare field. We aim to utilize DL and TL to assist in the early detection of skin cancer that can redirect to more targeted treatment approaches.