BMC Medical Research Methodology (Dec 2023)

The endorsement of general and artificial intelligence reporting guidelines in radiological journals: a meta-research study

  • Jingyu Zhong,
  • Yue Xing,
  • Junjie Lu,
  • Guangcheng Zhang,
  • Shiqi Mao,
  • Haoda Chen,
  • Qian Yin,
  • Qingqing Cen,
  • Run Jiang,
  • Yangfan Hu,
  • Defang Ding,
  • Xiang Ge,
  • Huan Zhang,
  • Weiwu Yao

DOI
https://doi.org/10.1186/s12874-023-02117-x
Journal volume & issue
Vol. 23, no. 1
pp. 1 – 13

Abstract

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Abstract Background Complete reporting is essential for clinical research. However, the endorsement of reporting guidelines in radiological journals is still unclear. Further, as a field extensively utilizing artificial intelligence (AI), the adoption of both general and AI reporting guidelines would be necessary for enhancing quality and transparency of radiological research. This study aims to investigate the endorsement of general reporting guidelines and those for AI applications in medical imaging in radiological journals, and explore associated journal characteristic variables. Methods This meta-research study screened journals from the Radiology, Nuclear Medicine & Medical Imaging category, Science Citation Index Expanded of the 2022 Journal Citation Reports, and excluded journals not publishing original research, in non-English languages, and instructions for authors unavailable. The endorsement of fifteen general reporting guidelines and ten AI reporting guidelines was rated using a five-level tool: “active strong”, “active weak”, “passive moderate”, “passive weak”, and “none”. The association between endorsement and journal characteristic variables was evaluated by logistic regression analysis. Results We included 117 journals. The top-five endorsed reporting guidelines were CONSORT (Consolidated Standards of Reporting Trials, 58.1%, 68/117), PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses, 54.7%, 64/117), STROBE (STrengthening the Reporting of Observational Studies in Epidemiology, 51.3%, 60/117), STARD (Standards for Reporting of Diagnostic Accuracy, 50.4%, 59/117), and ARRIVE (Animal Research Reporting of In Vivo Experiments, 35.9%, 42/117). The most implemented AI reporting guideline was CLAIM (Checklist for Artificial Intelligence in Medical Imaging, 1.7%, 2/117), while other nine AI reporting guidelines were not mentioned. The Journal Impact Factor quartile and publisher were associated with endorsement of reporting guidelines in radiological journals. Conclusions The general reporting guideline endorsement was suboptimal in radiological journals. The implementation of reporting guidelines for AI applications in medical imaging was extremely low. Their adoption should be strengthened to facilitate quality and transparency of radiological study reporting.

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