Reporting guidelines for studies involving generative artificial intelligence applications: what do I use, and when?
Bibliographic record
Abstract
The rise in publications addressing the use of general artificial intelligence (GAI), namely large language models (LLMs), for health purposes has generated the need to guide authors on transparent reporting practices 1 , 2 . Although LLMs currently dominate, other GAI applications such as diffusion models and large multimodal models are gaining popularity 3 . One key distinction between GAI and conventional AI is the ability of GAI to create new information based on its training data. Varying methodology and incomplete reporting among studies applying GAI for health purposes compromise the ability of readers to accurately interpret the study findings 3 , which is a particularly relevant issue when evaluating the effectiveness of complex GAI platforms in a healthcare context.
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 | 0.388 | 0.719 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.007 | 0.016 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.012 | 0.007 |
| Research integrity | 0.048 | 0.040 |
| Insufficient payload (model declined to judge) | 0.007 | 0.015 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".