Implementation of AI in radiology: the perspective of referring physicians
AI acceptance in radiology faces transparency and liability concerns. Lesion detection and data analysis were rated most beneficial by physicians. Clear regulation and explainability are key for clinical AI trust.
Authors
- Scherwin Mahmoudi (ORCID: https://orcid.org/0000-0002-4020-1873)
- Teodora Biciusca (ORCID: https://orcid.org/0000-0002-7423-2926)
- Tatjana Gruber‐Rouh (ORCID: https://orcid.org/0000-0001-9724-7744)
- Vitali Koch (ORCID: https://orcid.org/0000-0001-6915-5906)
- Aynur Gökduman
- İbrahim Yel (ORCID: https://orcid.org/0000-0002-3894-4782)
- Jennifer Gotta (ORCID: https://orcid.org/0000-0002-0524-6574)
- Simon Bernatz (ORCID: https://orcid.org/0000-0002-7758-8100)
- Elena Höhne
- Katrin Eichler
- Andreas Bucher (ORCID: https://orcid.org/0000-0001-9194-5955)
- Simon S. Martin (ORCID: https://orcid.org/0000-0003-3347-4520)
- Philipp Reschke (ORCID: https://orcid.org/0009-0006-9123-7581)
- Thomas J. Vogl (ORCID: https://orcid.org/0000-0002-4590-376X)
- Christian Wolfram
- Christian Booz
- Leon D. Grünewald
- Jan-Erik Scholtz
Institutions
- Goethe University Frankfurt (DE)
- University Hospital Frankfurt (DE)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2025-10-31
- DOI
- https://doi.org/10.1186/s13244-025-02120-4
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00