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

Institutions

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

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Insufficient reporting quality in large language model studies in the field of radiology

Chang-Yun Woo, Chong Hyun Suh, So Yeong Jeong, Pae Sun Suh et al.
Insights into Imaging
Artificial Intelligence in Healthcare and Education
article

Insufficient reporting quality in large language model studies in the field of radiology

Chang-Yun Woo, Chong Hyun Suh, So Yeong Jeong, Pae Sun Suh, Daiju Ueda, Hwon Heo, Woo Hyun Shim, Hyungjun Park
article en

Abstract

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.

Insights into ImagingVol. 17(1)
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)
Asan Institute for Life Sciences, Asan Medical Center
Quality Education
Openalex Percentile: Top 22%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Insufficient reporting quality in large language model studies in the field of radiology — Chang-Yun Woo, Chong Hyun Suh, et al. · Insights into Imaging (2026) | TGRS Research Map | TGRS