Reliable and Sustainable AI in Medical Imaging: Successes, Challenges, Limitations | Gitta Kutyniok

Abstract
Deep neural networks as the current work horse of artificial intelligence have already been tremendously successful in real-world applications, ranging from science to public life. The area of (medical) imaging sciences has been particularly impacted by deep learning-based approaches, which sometimes by far outperform classical approaches for particular problem classes. However, one current major drawback is the lack of reliability of such methodologies.

In this lecture we will first provide an introduction into this vibrant research area. We will then present some recent advances, in particular, concerning optimal combinations of traditional model-based methods with AI-based approaches in the sense of true hybrid algorithms, with a particular focus on limited-angle computed tomography. Due to the importance of explainability for reliability, we will also touch upon this area by highlighting an approach which is itself reliable due to its mathematical foundation. We will finish with a look into the future of (green) AI computing.