Simulating workload reduction with an AI-based prostate cancer detection pathway using a prediction uncertainty metric
Question AI can autonomously assess prostate MRI scans with high certainty at a non-inferior performance compared to radiologists, potentially reducing the workload of radiologists. Findings The optimal ratio of AI-model and radiologist readings is institute-dependent and requires calibration. Clinical relevance Semi-autonomous AI-based prostate cancer detection with variational UQ scores shows promise in reducing the number of scans radiologists need to read.
Authors
- Joeran S. Bosma (ORCID: https://orcid.org/0000-0002-6315-124X)
- Thomas C. Kwee (ORCID: https://orcid.org/0000-0001-9005-8529)
- Stefan J. Fransen (ORCID: https://orcid.org/0000-0002-5454-5052)
- Quintin van Lohuizen (ORCID: https://orcid.org/0000-0002-7128-0818)
- HenkJan J. Huisman (ORCID: https://orcid.org/0000-0001-6753-3221)
- Christian Roest (ORCID: https://orcid.org/0000-0002-3575-4465)
- Frank F.J. Simonis (ORCID: https://orcid.org/0000-0002-0734-1778)
- Derya Yakar (ORCID: https://orcid.org/0000-0002-4920-9441)
Institutions
- University Medical Center Groningen (NL)
- Radboud University Nijmegen (NL)
- Radboud University Medical Center (NL)
- University of Twente (NL)
Publication Details
- Journal
- European Radiology
- Published
- 2025-06-07
- DOI
- https://doi.org/10.1007/s00330-025-11727-6
- Citations
- 3
- Primary Topic
- Prostate Cancer Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 4.56
Funders
- Health~Holland
- Siemens Healthineers