ArticlePlastic and reconstructive surgery. Global open2026
Article in Plastic and reconstructive surgery. Global open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The rapid integration of generative AI tools into medical publishing has introduced new opportunities and risks, particularly in the creation of visual materials, such as anatomical illustrations. This study assessed the prevalence and characteristics of AI-generated facial anatomy images across peer-reviewed journals and online course promotional materials in plastic surgery and cosmetic dermatology. A total of 4734 manuscripts were screened, yielding 37 articles containing facial anatomical depictions. AI-generated content was identified in 10.8% (4 of 37) of relevant articles published in 2025, representing a marked increase from 2024, with all identified images demonstrating gross anatomical inaccuracies. A complementary web search of online anatomy courses also found that nearly half of the sites used erroneous AI-generated imagery in promotional content. These findings underscore growing concerns over the rapid and unregulated adoption of powerful AI tools for visual content in medical publishing, with regard to scientific misinformation, legal risks, and professional standards. In this special topic discussion, we describe how generative AI models function, outline their limitations, and explore the ethical, legal, and professional implications of their misuse. To ensure the safe and responsible integration of visual AI generators in medical publishing, the urgent adoption of clear standards and oversight protocols should be considered for anatomical and surgical depictions.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.