Use of AI in Family Medicine Publications: A Joint Editorial From Journal Editors
Bibliographic record
Abstract
There are multiple guidelines from publishers and organizations on the use of artiXcial intelligence (AI) in publishing.However, none are speciXc to family medicine.Most journals have some basic AI use recommendations for authors, but more explicit direction is needed, as not all AI tools are the same.As family medicine journal editors, we want to provide a uniXed statement about AI in academic publishing for authors, editors, publishers, and peer reviewers based on our current understanding of the Xeld.The technology is advancing rapidly.While text generated from early large language models (LLMs) was relatively easy to identify, text generated from newer versions is getting progressively better at imitating human language and more challenging to detect.Our goal is to develop a uniXed framework for managing AI in family medicine journals.As this is a rapidly evolving environment, we acknowledge that any such framework will need to continue to evolve.However, we also feel it is important to provide some guidance for where we are today. De#nitionsArtiXcial intelligence (AI) is a broad Xeld where computers perform tasks that have historically been thought to require human intelligence.LLMs are a recent breakthrough in AI that allow computers to generate text that seems like it comes from a human.LLMs deal with language generation, while the broader term "generative AI" can also include AI-generated images or Xgures.ChatGPT is one of the earliest and widely used LLM models, but other companies have developed similar products.LLMs "learn" to do a multifaceted analysis of word sequences in a massive text training database and generate new sequences of words using a complex probability model.The model has a random component, so responses to the exact same prompt submitted multiple times will not be identical.LLMs can generate text that looks like a medical journal article in response to a prompt, but the article's content may or may not be accurate.LLMs may "confabulate" generating convincing text that includes false information.LLMs do not search the internet for answers to questions.However, they have been paired with search engines in increasingly sophisticated ways.For the rest of this editorial, we will use the broad term "AI" synonymously with LLMs.
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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 | 0.023 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.016 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.019 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.007 |
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".