Automation bias in AI-assisted detection of cerebral aneurysms on time-of-flight MR angiography
Our results demonstrate the susceptibility of radiology readers to automation bias in detecting cerebral aneurysms in TOF-MRA studies when encountering false-positive AI findings. While AI systems for cerebral aneurysm detection can provide benefits, challenges in human-AI interaction need to be mitigated to ensure safe and effective adoption.
Authors
- Jan S. Kirschke (ORCID: https://orcid.org/0000-0002-7557-0003)
- Dominik Sepp (ORCID: https://orcid.org/0000-0002-4565-4409)
- Christian Maegerlein (ORCID: https://orcid.org/0000-0003-4885-7671)
- Benedikt Wiestler (ORCID: https://orcid.org/0000-0002-2963-7772)
- Severin Schramm (ORCID: https://orcid.org/0000-0002-3299-5861)
- Lena Schmitzer (ORCID: https://orcid.org/0000-0003-1644-3335)
- Enrike Rosenkranz
- Jannis Bodden (ORCID: https://orcid.org/0000-0001-5997-203X)
- Dennis M. Hedderich (ORCID: https://orcid.org/0000-0001-8994-5593)
- Tobias Boeckh‐Behrens (ORCID: https://orcid.org/0000-0003-0266-8947)
- Su Hwan Kim (ORCID: https://orcid.org/0000-0002-5383-2041)
- Martin Renz (ORCID: https://orcid.org/0000-0001-6644-5342)
- Thomas Baum (ORCID: https://orcid.org/0000-0002-4574-5212)
- Olivia Kertels (ORCID: https://orcid.org/0000-0001-7144-1901)
- Karolin J. Paprottka
- Claus Zimmer (ORCID: https://orcid.org/0000-0002-4805-9103)
- Evamaria Olga Riedel
Institutions
- TUM Klinikum (DE)
- Technical University of Munich (DE)
Publication Details
- Journal
- La radiologia medica
- Published
- 2025-02-12
- DOI
- https://doi.org/10.1007/s11547-025-01964-6
- Citations
- 15
- Primary Topic
- Intracranial Aneurysms: Treatment and Complications
- Type
- article
- Field-Weighted Citation Impact
- 20.84
Funders
- Technische Universität München