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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Radiology

Background:

  • Artificial intelligence (AI) is increasingly utilized in scientific research.
  • Sharing code and data is fundamental for scientific reproducibility and transparency.

Purpose of the Study:

  • To evaluate code and data sharing practices in original AI scientific manuscripts.
  • To assess the reproducibility of shared code in AI research.
  • To analyze trends in AI research sharing from 2017-2021 within RSNA journals.

Main Methods:

  • Retrospective meta-research study of 218 AI articles published in RSNA journals (2017-2021).
  • Evaluation of code sharing, code reproducibility, and data sharing practices.
  • Statistical analysis using Fisher exact tests to compare sharing rates by year, journal, and algorithm type.

Main Results:

  • Only 34% of articles shared code, with 11% sharing reproducible code.
  • Data sharing was reported in 13% of articles, with 41% sharing complete experimental data.
  • Code sharing rates increased significantly in 2020-2021 and were higher in "Radiology" and "Radiology: Artificial Intelligence" journals.

Conclusions:

  • Original AI scientific articles in RSNA journals exhibit low rates of code and data sharing.
  • There is a significant need to promote open-source code and data sharing for transparent and reproducible AI science.
  • Future efforts should focus on enhancing the accessibility and reusability of AI research outputs.