Perspectives on Artificial Intelligence in Medical Publishing: A Survey of Medical Journal Editors
Bibliographic record
Abstract
ABSTRACT: Artificial intelligence (AI) has been increasingly integrated into medical publishing, hopefully improving efficiency and accuracy, but serious concerns persist regarding ethical implications, authorship attribution, and content reliability. We aimed at understanding the perspectives of editors of medical journals on AI. A structured online questionnaire was developed and distributed to editors-in-chief of medical journals worldwide. The survey comprised 27 concise questions exploring demographics, journal practices, and perspectives on AI in editorial workflows. Quantitative data were analyzed using descriptive statistics to summarize usage patterns, perceived benefits, risks, and future expectations. A total of 59 editors-in-chief completed the survey (response rate: 19%), with replies suggesting substantial variability in beliefs and attitudes toward AI for publication in medical journals. Artificial intelligence tools were already in use by 49% of journals, mainly for plagiarism detection (76%) and data verification (35%). Only 9% of responders reported that journals used AI for both scientific and linguistic review. Time savings (79%) and cost reduction (43%) were the most commonly cited benefits, and concerns included potential bias (71%) and lack of accountability (60%). Overall, 81% of responders anticipated a major role for AI in publishing within 10 years. Exploratory analyses suggested several potential associations between replies and respondent or journal features, requiring further validation in future surveys. In conclusion, this survey on attitudes toward AI in publication in medical journals suggests that editors-in-chief are cautiously adopting AI in their editorial workflow, supporting its operational use while explicitly calling for clear guidance to address ethical and regulatory concerns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".