Female Authorship Trends Among Articles About Artificial Intelligence in North American Radiology Journals
Bibliographic record
Abstract
Purpose: To examine trends in female authorship of peer-reviewed North American radiology articles centred around artificial intelligence (AI). Method: A bibliographic search was conducted for all AI-related articles published in four North American radiology journals. Collected data included the genders of the first and last (senior) authors, year and country. We compared the trends of female authorship using Pearson chi-square, Fisher exact tests and logistic regression models. Results: 453 articles met the inclusion criteria. Among these, 107 (22.3%) had a female first author and 97 (27.3%) had a female senior author. Female first authors were over three times more likely to publish with a female senior author. Among the four journals, the CARJ had the highest proportion of female senior authors at 45.5%. The only significant temporal trend identified was an increase over the years in female senior authors in Radiology. Twenty-four countries contributed to the included articles, with the largest contributors being the United States (n = 290) and Canada (n = 30). Of the countries contributing more than 15 articles, there were none with above 50% female authorship. Conclusions: Female authors are underrepresented in AI-related radiology literature. However, there has been an encouraging recent increase in female authorship in AI-related radiology articles trending towards significance. There is a great opportunity to improve female representation in AI with intentional mentorship and recruitment. We urge more platforms for female voices in radiology as AI becomes increasingly integrated into the radiology community.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.025 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".