Enhanced guidance on artificial intelligence for medical publication and communication professionals
Bibliographic record
Abstract
The International Society for Medical Publication Professionals (ISMPP) position statement and call to action on the use of artificial intelligence (AI), published in 2024, recognized the value of AI while advocating for best practices to guide its use. In this commentary, we offer enhanced guidance on the call to action for ISMPP members and other medical communication professionals on the topics of education and training, implementation and use, and advocacy and community engagement. With AI rapidly revolutionizing scientific communication, members should stay up to date with advancements in the field by completing AI training courses, engaging with ISMPP AI education and training and other external training platforms, developing a practice of lifelong learning, and improving AI literacy. Members can successfully integrate and use AI by complying with organizational policies, ensuring fair access to AI models, complying with authorship guidance, properly disclosing the use of AI models or tools, respecting academic integrity and copyright restrictions, and understanding privacy protections. Members also need to be familiar with the systemic problem of bias with large language models, which can reinforce health inequities, as well as the limits of transparency and explainability with AI models, which can undermine source verification, bias detection, and even scientific integrity. AI models can produce hallucinations, results that are factually incorrect, irrelevant, or nonsensical, which is why all outputs from AI models should be reviewed and verified for accuracy by humans. With respect to advocacy and community engagement, members should advocate for the responsible use of AI, participate in developing AI policy and governance, work with underserved communities to get access to AI tools, and share findings for AI use cases or research results in peer-reviewed journals, conferences, and other professional platforms.
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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 | 0.139 | 0.425 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.030 | 0.034 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.098 | 0.072 |
| Insufficient payload (model declined to judge) | 0.020 | 0.022 |
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".