BACKGROUND Since 2017, there have been several reports of artificial intelligence (AI) achieving comparable performance to human experts on medical image analysis tasks. With the first ratification of a computer vision algorithm as a medical device in 2018, the way was paved for these methods to eventually become an integral part of modern clinical practice. OBJECTIVE The purpose of this article is to review the main developments that have occurred over the last few years in AI for image analysis, in relation to clinical applications and dermatology. METHODS Following the annual ImageNet challenge, we review classical methods of machine learning for image analysis and demonstrate how these methods incorporated human expertise but failed to meet industrial requirements regarding performance and scalability. With the rise of deep learning based on artificial neural networks, these limitations could be overcome. We discuss important aspects of this technology including transfer learning and report on recent developments such as explainable AI and generative models. RESULTS Deep learning models achieved performance on a par with human experts in a broad variety of diagnostic tasks and were shown to be suitable for industrialization. Therefore, current developments focus less on further improving accuracy but rather address open issues such as interpretability and applicability under clinical conditions. Upcoming generative models allow for entirely new applications. CONCLUSIONS Deep learning has a history of remarkable success and has become the new technical standard for image analysis. The dramatic improvement these models brought over classical approaches enables applications in a rapidly increasing number of clinical fields. In dermatology, as in many other domains, artificial intelligence still faces considerable challenges but is undoubtedly developing into an essential tool of modern medicine.