Artificial Intelligence in Radiology: A Position Paper of the Austrian Radiological Society (ÖRG).
Authors
Affiliations (22)
Affiliations (22)
- Villach Regional Hospital, Institute of Diagnostic and Interventional Radiology, Austria, Villach.
- Medical University of Graz, Department of Radiology, Austria, Graz.
- Danube Private University, Research Center for Medical Image Analysis and Artificial Intelligence (MIAAI), Austria, Krems an der Donau.
- Medical University of Graz, Radiology, Austria, Graz.
- Medical University of Innsbruck, Department of Radiology, Austria, Innsbruck.
- Hospital of St. John of God, Department of Radiology and Nuclear Medicine, Austria, Vienna.
- Medical University of Innsbruck, Institute of Neuropathology and Neuromolecular Pathology, Austria, Innsbruck.
- Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Austria, Vienna.
- Danube Private University, Research Center for Medical Image Analysis and Artificial Intelligence, Department of Medicine, Austria, Krems an der Donau.
- Lund University Faculty of Medicine, Division of Diagnostic Radiology, Department of Translational Medicine, Sweden, Lund.
- Skåne University Hospital Lund, Unilabs Mammography Unit, Sweden, Lund.
- University of Graz, Division of Health Care Ethics, Austria, Graz.
- KAGes, Directorate for Digitalization, IT, Organizational Development and Innovation, Austria, Graz.
- Radiology Group Practice Baden, Radiological Group Practice, Austria, Baden.
- Medical University of Vienna, Department of General and Pediatric Radiology, Austria, Vienna.
- Regional Hospital of Bregenz, Department of Radiology, Austria, Bregenz.
- University of Vienna Faculty of Law, Department of Innovation and Digitalisation in Law, Austria, Vienna.
- Paracelsus Medical University Salzburg - private foundation, Department of Laboratory Medicine, Austria, Salzburg.
- University of Vienna Faculty of Social Sciences, Department of Political Science, Austria, Vienna.
- Medical University of Vienna, Department of Biomedical Imaging and Image-guided Therapy, Computational Imaging Research Lab, Austria, Vienna.
- Hochsteiermark Regional Hospital Leoben Location, Department of Radiology and Nucelar Medicine, Austria, Leoben.
- Medical University of Graz, Division of General Radiology, Department of Radiology, Austria, Graz.
Abstract
This position paper was written at the request of the executive board of the Austrian Radiological Society. As a co-opted member of the executive board of the Austrian Radiological Society for digitalization and artificial intelligence (AI), the corresponding author was tasked with assembling an interdisciplinary group of authors. The authors were selected on the basis of their scientific and technical expertise, their position at clinical and university facilities, and their involvement in national and international professional societies and working groups related to AI in radiology. Various radiology subspecialties, the major university health care regions in Austria, and health facilities on various care levels are represented. Experts from the fields of technology, law, and ethics were also included. The referenced literature is based on targeted research without a systematic meta-analysis. The individual chapters were written by topic-specific subgroups and the chapters were then combined by the first author to create this manuscript. The Delphi method was not used. The expert consensus was reviewed by the executive board of the Austrian Radiological Society and approved as the official position of the society after revision. · Safe implementation of commercial AI requires standardized local validation, clearly defined responsibilities, continuous performance monitoring, version control, and established procedures for error reporting, revalidation, and deactivation.. · AI performance cannot be established by regulatory approval or technical benchmarks alone but must be demonstrated under local clinical conditions.. · Human responsibility remains essential despite increasing automation.. · AI is shifting the role of radiology from primarily image interpretation toward an increasingly data-integrative, prognostic, and interdisciplinary discipline.. · The value of AI should be assessed by patient-relevant outcomes and effects on healthcare delivery rather than by technical performance or workforce reduction alone.. · Kau T, Furtner-Srajer J, Adelsmayr G et al. Artificial Intelligence in Radiology: A Position Paper of the Austrian Radiological Society (ÖRG). Rofo 2026; DOI 10.1055/a-2965-2624.