Insufficient reporting quality in large language model studies in the field of radiology
Numerous studies on large language models (LLMs) in radiology lack standardized methodologies, leading to high variability and inconsistent reporting. Our review demonstrated insufficiency in key elements for LLM research, particularly in model details and output probability. Better reporting and adherence to key elements are essential for enhancing transparency and reproducibility in future LLM research.
Authors
- Chang-Yun Woo (ORCID: https://orcid.org/0000-0002-8286-8481)
- Chong Hyun Suh (ORCID: https://orcid.org/0000-0002-4737-0530)
- So Yeong Jeong (ORCID: https://orcid.org/0000-0003-4705-0008)
- Pae Sun Suh
- Daiju Ueda
- Hwon Heo
- Woo Hyun Shim
- Hyungjun Park
Institutions
- Ulsan College (KR)
- Yonsei University (KR)
- Asan Medical Center (KR)
- Seoul National University Bundang Hospital (KR)
- University of Ulsan (KR)
- Shinhwa Medical (South Korea) (KR)
- Osaka Metropolitan University (JP)
- Tokyo Metropolitan University (JP)
Publication Details
- Journal
- Insights into Imaging
- Published
- 2026-03-16
- DOI
- https://doi.org/10.1186/s13244-026-02236-1
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Asan Institute for Life Sciences, Asan Medical Center