Reporting Quality of Large Language Model Studies: A Cross-Sectional Audit of High-Ranking Radiology and Medical Imaging Journals.

objectiveTo evaluate adherence to the Minimum Reporting Items for Clear Evaluation of Accuracy Reports of Large Language Models in Healthcare (MI-CLEAR-LLM) in radiology and medical imaging studies involving large language models (LLMs). MATERIALS AND

methodsWe conducted a cross-sectional audit of original LLM research studies published between January 1 and December 26, 2025, in Q1 journals within the Web of Science "Radiology, Nuclear Medicine, and Medical Imaging" category. PubMed and Scopus were searched to identify eligible studies. A quota-based subsampling strategy, based on journal publication volume, was used to select approximately 100 studies. All four eligible articles from the

resultsOf 201 eligible studies identified, 102 were finally analyzed after applying the subsampling strategy. Overall adherence to MI-CLEAR-LLM was moderate (mean, 51.2% ± 14.7%; range, 22.2%-84.2%). Adherence was highest for input data type (100%), test-data independence (80.2%), and adaptation strategy (78.1%), and lowest for prompt execution setup (29.4%) and stochasticity management (33.1%). The least frequently reported items were training-data cutoff date (9.8%) and rationale for prompt wording (15.6%). Adherence varied significantly across journals (

conclusionReporting transparency in radiology and medical imaging LLM studies published in 2025 was inconsistent across reporting items and journals, with substantial deficiencies in some reproducibility-critical elements. Broader adoption of reporting standards is essential to improve the reproducibility and interpretability of future accuracy evaluations.