Artificial intelligence (AI) comprises computational methods capable of tasks associated with human cognition, and includes specialized subfields such as machine learning, deep learning, convolutional neural networks for image analysis, and large language models for text-based workflows. In dermatology, these methods are increasingly used across research and clinical practice, supporting drug discovery, diagnosis, decision-making, and personalized treatment. In drug discovery, AI can accelerate target identification, support in silico compound design, and predict the effectiveness and safety profiles of compounds. It also offers opportunities for drug repurposing, helping identify candidates for conditions with limited treatment options. In clinical practice, AI enhances teledermatology and imaging-based assessment, providing consistent lesion analysis, severity evaluation, and triage support across malignant, inflammatory, and infectious skin diseases. Beyond diagnosis, AI is beginning to play a significant role in supporting therapeutic decision-making. Clinical decision-support systems and multimodal AI tools assist clinicians by organizing information, suggesting management strategies, monitoring disease progression, and helping align treatment choices with current guidelines. AI models can further estimate treatment response, durability, and the likelihood of therapy adjustment, creating a basis for more individualized care. Despite these advances, significant challenges remain. Bias in training data, limited real-world validation, unclear regulatory frameworks, concerns over interpretability, and the need for patient and clinician trust remain ongoing barriers. This review aims to provide an up-to-date overview of the emerging application domains of AI in dermatology.