ArticleThe journal of allergy and clinical immunology. Global2025
Three versions of an atopic dermatitis case report written by humans, artificial intelligence, or both: Identification of authorship and preferences.
Article in The journal of allergy and clinical immunology. Global, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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
1 citing paper in PubMed.
- A scoping review of human-AI collaboration patterns and task divisions in healthcare applications.NPJ digital medicine · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: The use of artificial intelligence (AI) in scientific writing is rapidly increasing, raising concerns about authorship identification, content quality, and writing efficiency. Objectives: This study investigates the real-world impact of ChatGPT, a large language model, on those aspects in a simulated publication scenario. Methods: Forty-eight individuals representing 3 medical expertise levels (medical students, residents, and experts in allergy or dermatology) evaluated 3 blinded versions of an atopic dermatitis case report: one each human written (HUM), AI generated (AI), and combined written (COM). The survey assessed authorship, ranked their preference, and graded 13 quality criteria for each text. Time taken to generate each manuscript was also recorded. Results: Authorship identification accuracy mirrored the odds at 33%. Expert participants (50.9%) demonstrated significantly higher accuracy compared to residents (27.7%) and students (19.6%, Conclusion: AI can deceptively mimic human writing, particularly for less experienced readers. Although AI-assisted writing is appealing and offers significant time savings, human oversight remains crucial to ensure accuracy, ethical considerations, and optimal quality. These findings underscore the need for transparency in AI use and highlight the potential of human-AI collaboration in the future of scientific writing.
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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.