Availability and transparency of artificial intelligence models in radiology: a meta-research study
Question The study addresses the limited availability of AI models in radiology, especially DL models, which impacts external validation and clinical reliability. Findings Only 39.9% of radiology AI studies made their models available, with DL models showing particularly low availability at 11.5%. Clinical relevance Improving the availability of radiology AI models is essential for enabling external validation, ensuring reliable clinical application, and advancing patient care by fostering robust and transparent AI systems.
Authors
- Seong Ho Park (ORCID: https://orcid.org/0000-0002-1257-8315)
- Soon Ho Yoon (ORCID: https://orcid.org/0000-0002-3700-0165)
- Jong Hyuk Lee (ORCID: https://orcid.org/0000-0002-9594-683X)
- Taehee Lee (ORCID: https://orcid.org/0000-0002-0592-8268)
- Hyungjin Kim (ORCID: https://orcid.org/0000-0003-0722-0033)
Institutions
- Ulsan College (KR)
- Seoul National University (KR)
- Asan Medical Center (KR)
- New Generation University College (ET)
- Seoul National University Hospital (KR)
- University of Ulsan (KR)
- National University College (PR)
Publication Details
- Journal
- European Radiology
- Published
- 2025-03-17
- DOI
- https://doi.org/10.1007/s00330-025-11492-6
- Citations
- 2
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- National Research Foundation
- Seoul National University
- National Research Foundation of Korea
- Ministry of Education, Science and Technology
- Ministry of Science and ICT, South Korea