Technical progress in the temporally and spatially high-resolution recording of image data and their storage enables completely new types of scientific knowledge and at the same time creates high demands on data analysis, which is typically carried out using artificial intelligence methods. For this reason, professorships for artificial intelligence in image processing are currently being established at universities and university hospitals with a corresponding research profile. In order to meet the special requirements for the development of methods for the analysis of neural image data, professorships are currently being established that explicitly focus on neuroscientific applications. These special requirements concern on the one hand the demands of the sheer size of (neuro-)imaging cohort studies with tens of thousands of data sets, and on the other hand the integration of multimodal data and the application of multivariate methods and methods for single case prediction. With currently established approaches in neuroimaging, these requirements can only be insufficiently solved, which is why a methodological further development of algorithms for the evaluation of imaging data using artificial intelligence is urgently required.