ABSTRACT
This chapter provides a comprehensive overview of artificial intelligence (AI) technologies and their transformative role in dermatology, particularly in skin cancer detection. Beginning with the evolution of AI from rule-based systems to deep learning, it emphasizes the importance of data acquisition, augmentation, and preprocessing in building reliable diagnostic models. Core tools such as TensorFlow, PyTorch, and Keras are discussed alongside hardware and software requirements essential for effective AI development. The chapter also explores AI’s integration into assistive technologies for visually or physically impaired users, highlighting inclusivity and accessibility in healthcare. Additionally, it addresses critical challenges—data imbalance, algorithmic bias, interpretability, and regulatory issues—while underscoring future opportunities like explainable AI, federated learning, and edge computing. Overall, this chapter bridges technical innovation with clinical application to advance equitable, efficient, and ethical AI-driven dermatology.
