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Technical innovations in image acquisition methods with higher spatial, temporal, and functional resolution have increased imaging data production rates tremendously. This poses a significant challenge to radiologists, who are limited by image processing and understanding capabilities of the human brain. Hence, the critical bottleneck for medical diagnosis is no longer the acquisition of images, but their timely and accurate interpretation. Facing this challenge, artificial intelligence (AI) will have a major impact on how we will practice radiology in the future: AI will revolutionize imaging interpretation and protocoling, reduce radiation exposure and contrast agent dosage, streamline patient scheduling, and support efficient communication of clinically meaningful imaging information to referring physicians and their patients. Hence, it is evident that AI will re-define all aspects of the radiology profession by making radiologists better and faster at what they do. Here, innovative approaches to machine learning on large multidimensional spatiotemporal data play a key role for improving image understanding in biomedicine. Current accomplishments in the presenter’s research projects address scientific challenges in machine learning, including explainable artificial intelligence, machine-supported image annotation, human-machine complementarity, and unsupervised exploratory data visualization. Clinical applications include automatic analysis of chest radiographs, imaging biomarkers for breast cancer and brain tumor diagnosis, non-invasive bone stability prediction in osteoporosis, novel methods for imaging of neuroinflammatory disease, and recent breakthroughs in brain network connectivity analysis for the diagnosis of neurologic disorders. An outlook to clinical deployment and quantitative evaluation of artificial intelligence solutions in radiology confirms the broad applicability of the presented methods.
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