Artificial intelligence in radiology - beyond the black box.

Journal: RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin
Published Date:

Abstract

BACKGROUND: Artificial intelligence is playing an increasingly important role in radiology. However, more and more often it is no longer possible to reconstruct decisions, especially in the case of new and powerful methods from the field of deep learning. The resulting models fulfill their function without the users being able to understand the internal processes and are used as so-called black boxes. Especially in sensitive areas such as medicine, the explainability of decisions is of paramount importance in order to verify their correctness and to be able to evaluate alternatives. For this reason, there is active research going on to elucidate these black boxes.

Authors

  • Luisa Gallée
    Division of Experimental Radiology, Department for Diagnostic and Interventional Radiology, University Ulm Medical Centre, Ulm, Germany.
  • Hannah Kniesel
    Visual Computing, University of Ulm, Germany.
  • Timo Ropinski
    Visual Computing Group, Institute of Media Informatics, Ulm University, Ulm, Germany.
  • Michael Götz
    Medical Image Analysis, Division Medical Image Computing, DKFZ Heidelberg, Germany; National Center for Tumor Diseases (NCT), Heidelberg, Germany. Electronic address: m.goetz@dkfz-heidelberg.de.