Medizinische Universität Graz Austria/Österreich - Forschungsportal - Medical University of Graz
Gewählte Publikation:
SHR Neuro Krebs Kardio Lipid Stoffw Microb
Evans, T; Retzlaff, CO; Geissler, C; Kargl, M; Plass, M; Muller, H; Kiehl, TR; Zerbe, N; Holzinger, A.
The explainability paradox: Challenges for xAI in digital pathology
FUTURE GENER COMP SY. 2022; 133: 281-296.
Doi: 10.1016/j.future.2022.03.009
Web of Science
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- Co-Autor*innen der Med Uni Graz
- Holzinger Andreas
- Kargl Michaela
- Müller Heimo
- Plass Markus
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- Abstract:
- The increasing prevalence of digitised workflows in diagnostic pathology opens the door to life-saving applications of artificial intelligence (AI). Explainability is identified as a critical component for the safety, approval and acceptance of AI systems for clinical use. Despite the cross-disciplinary challenge of building explainable AI (xAI), very few application-and user-centric studies in this domain have been carried out. We conducted the first mixed-methods study of user interaction with samples of stateof-the-art AI explainability techniques for digital pathology. This study reveals challenging dilemmas faced by developers of xAI solutions for medicine and proposes empirically-backed principles for their safer and more effective design. (C) 2022 The Authors. Published by Elsevier B.V.
- Find related publications in this database (Keywords)
- Explainable AI
- Digital pathology
- Usability
- Trust
- Artificial intelligence