Evaluation of commercial AI algorithms for the detection of fractures, effusions, and dislocations on real-world clinical data: A prospective registry study
Current tools should be used as adjuncts rather than replacements for radiologists and reporting radiographers. Multicenter validation and more diverse training data are necessary to improve generalizability and robustness.
Authors
- Christian Mertens (ORCID: https://orcid.org/0000-0001-7303-2650)
- Lisa C. Adams (ORCID: https://orcid.org/0000-0001-5836-4542)
- Keno K. Bressem (ORCID: https://orcid.org/0000-0001-9249-8624)
- Tristan Lemke (ORCID: https://orcid.org/0009-0006-4269-142X)
- David W. Weller (ORCID: https://orcid.org/0000-0001-6558-4164)
- Sebastian Ziegelmayer (ORCID: https://orcid.org/0000-0001-8724-4718)
- Felix Busch (ORCID: https://orcid.org/0000-0001-9770-8555)
- Antonín Komenda (ORCID: https://orcid.org/0000-0002-6947-308X)
- Marcus R. Makowski (ORCID: https://orcid.org/0000-0001-8778-647X)
- Su Hwan Kim (ORCID: https://orcid.org/0000-0002-5383-2041)
- Ina Luiken (ORCID: https://orcid.org/0000-0002-5961-4002)
- Alexander Wolfgang Marka (ORCID: https://orcid.org/0000-0002-2111-8177)
- Philipp Prucker (ORCID: https://orcid.org/0000-0002-6968-2156)
- Markus Graf
Institutions
- TUM Klinikum (DE)
Publication Details
- Journal
- Radiography
- Published
- 2025-10-01
- DOI
- https://doi.org/10.1016/j.radi.2025.103189
- Citations
- 3
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 1.96