Consistent performance between medical experts and non‐expert readers in forced‐choice lesion‐detection tasks with PET images
We did not find substantive differences between the average performance of medical experts and non-experts. This suggests that in simple detection tasks like these, clinical experience is not necessary, and we can use non-medical observers instead of physicians when training models.
Authors
- Darrin Byrd
- Delphine L. Chen (ORCID: https://orcid.org/0000-0002-0150-5401)
- Amir Iravani (ORCID: https://orcid.org/0000-0002-1273-5835)
- Kristen A. Wangerin (ORCID: https://orcid.org/0000-0002-9099-3223)
- Murat Sadıç
- Craig K. Abbey (ORCID: https://orcid.org/0000-0003-4608-9402)
- Fatemeh Behnia (ORCID: https://orcid.org/0000-0002-9110-909X)
- Sangtae Ahn (ORCID: https://orcid.org/0000-0001-7252-2607)
- Muhan Shao (ORCID: https://orcid.org/0000-0002-4686-4395)
- Paul E. Kinahan (ORCID: https://orcid.org/0000-0001-6461-3306)
- Scott D. Wollenweber (ORCID: https://orcid.org/0000-0001-6105-6831)
- Jean H. Lee
Institutions
- University of California, Santa Barbara (US)
- University of Washington (US)
- GE Global Research (United States) (US)
Publication Details
- Journal
- Medical Physics
- Published
- 2025-10-22
- DOI
- https://doi.org/10.1002/mp.70074
- Primary Topic
- Radiomics and Machine Learning in Medical Imaging
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Institutes of Health
- National Cancer Institute