The policies on the use of large language models in radiological journals are lacking: a meta-research study
Jingyu Zhong, Yue Xing, Yangfan Hu, Junjie Lu, Jiarui Yang, Guangcheng Zhang, Shiqi Mao, Haoda Chen, Qian Yin, Qingqing Cen, Run Jiang, Jingshen Chu, Yang Song, Minda Lu, Defang Ding, Xiang Ge, Huan Zhang, Weiwu Yao

TL;DR
This study finds that most radiology journals lack clear policies on using large language models, and suggests creating a shared reporting guideline to improve transparency.
Contribution
The study is the first to systematically evaluate LLM use policies in radiological journals and identifies gaps in current guidelines.
Findings
Only 43.9% of radiological journals have LLM use policies.
Fewer than 5% of journals address the potential influence of LLMs in their policies.
The presence of LLM policies is strongly associated with the journal's publisher.
Abstract
To evaluate whether and how the radiological journals present their policies on the use of large language models (LLMs), and identify the journal characteristic variables that are associated with the presence. In this meta-research study, we screened Journals from the Radiology, Nuclear Medicine and Medical Imaging Category, 2022 Journal Citation Reports, excluding journals in non-English languages and relevant documents unavailable. We assessed their LLM use policies: (1) whether the policy is present; (2) whether the policy for the authors, the reviewers, and the editors is present; and (3) whether the policy asks the author to report the usage of LLMs, the name of LLMs, the section that used LLMs, the role of LLMs, the verification of LLMs, and the potential influence of LLMs. The association between the presence of policies and journal characteristic variables was evaluated. The…
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Taxonomy
TopicsDiverse Educational Innovations Studies
