  Computer-aided diagnostics has benefited from the development of deep
learning-based computer vision techniques in these years. Traditional
supervised deep learning methods assume that the test sample is drawn from the
identical distribution as the training data. However, it is possible to
encounter out-of-distribution samples in real-world clinical scenarios, which
may cause silent failure in deep learning-based medical image analysis tasks.
Recently, research has explored various out-of-distribution (OOD) detection
situations and techniques to enable a trustworthy medical AI system. In this
survey, we systematically review the recent advances in OOD detection in
medical image analysis. We first explore several factors that may cause a
distributional shift when using a deep-learning-based model in clinic
scenarios, with three different types of distributional shift well defined on
top of these factors. Then a framework is suggested to categorize and feature
existing solutions, while the previous studies are reviewed based on the
methodology taxonomy. Our discussion also includes evaluation protocols and
metrics, as well as the challenge and a research direction lack of exploration.
