Generative AI Guidelines in Korean Medical Journals: A Survey Using Human-AI Collaboration
Bibliographic record
Abstract
Abstract Background Generative artificial intelligence (GAI) tools, such as large language models, have the potential to revolutionize medical research and writing, but their use also raises important ethical and practical concerns. This study examines the prevalence and content of GAI guidelines among Korean medical journals to assess the current landscape and inform future policy development. Methods Top 100 Korean medical journals by H-index were surveyed. Author guidelines were collected and screened by a human author and AI chatbot to identify GAI-related content. Key components of GAI policies were extracted and compared across journals. Journal characteristics associated with GAI guideline adoption were also analyzed. Results Only 18% of the surveyed journals had GAI guidelines, which is much lower than previously reported international journals. However, adoption rates increased over time, reaching 57.1% in the first quarter of 2024. Higher-impact journals were more likely to have GAI guidelines. All journals with GAI guidelines required authors to declare GAI use, and 94.4% prohibited AI authorship. Key policy components included emphasizing human responsibility (72.2%), discouraging AI-generated content (44.4%), and exempting basic AI tools (38.9%). Conclusion While GAI guideline adoption among Korean medical journals is lower than global trends, there is a clear increase in implementation over time. The key components of these guidelines align with international standards, but greater standardization and collaboration are needed to ensure responsible and ethical use of GAI in medical research and writing. Abstract Figure
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".