PGMI assessment in mammography: AI software versus human readers
AI has promising potential for automated assessment of diagnostic image quality. Faster, more representative and more objective feedback may support radiographers in their quality management processes. Direct transformation of common PGMI workflows into an AI algorithm could be challenging.
Authors
- Carlotta Ruppert (ORCID: https://orcid.org/0000-0003-4360-822X)
- Solveig Hofvind (ORCID: https://orcid.org/0000-0003-0178-8939)
- J.G. Stalheim
- Stefano Gianolini
- Simon Frei
- Vanessa Froehlich (ORCID: https://orcid.org/0000-0002-5497-7680)
- Gerlig Widmann (ORCID: https://orcid.org/0000-0002-7255-0672)
- Tina Santner
- M Hondl
Institutions
- Innsbruck Medical University (AT)
- Fachhochschule Wiener Neustadt (AT)
- Wiener Krankenanstaltenverbund (AT)
- Institute of Plant Biology (HU)
- Cancer Registry of Norway (NO)
- Evac (Norway) (NO)
- Software (Spain) (ES)
Publication Details
- Journal
- Radiography
- Published
- 2025-07-05
- DOI
- https://doi.org/10.1016/j.radi.2025.103017
- Citations
- 3
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 4.08