Authors
Khouloud Kchaou, Soumaya Rebay, Salma Mokaddem, Amani Sayhi, Fatma Guezguez, Leila Triki, Rim Kammoun, Yacine Ouahchi, Helmi Ben Saad
Published in
Journal of visual communication in medicine. Pages 1-9. Sep 01, 2026. Epub Sep 01, 2026.
Abstract
Graphical abstracts are increasingly used to enhance scientific communication, yet their quality remains variable. Generative artificial intelligence (AI) tools can produce graphical abstracts, but their scientific reliability has not been systematically evaluated. To assess the scientific quality of AI-generated graphical abstracts in respiratory medicine. This multicentre study included a pilot phase (5 abstracts; 15 graphical abstracts) and a main phase (20 abstracts; 60 graphical abstracts). For each text abstract, three graphical abstracts were generated via ChatGPT (version 5.2), Claude Sonnet (version 4.5), and Gemini (version 3), using a standardised prompt. Six experts evaluated each graphical abstract using a 7-item scoring grid (total score/28). Inter-rater reliability and comparisons between models were assessed.The median total score was 21 [16-25]. Single-measure intraclass correlation coefficients (ICCs) indicated moderate agreement for the total score (ICC = 0.606), while average-measure ICC showed excellent reliability (ICC = 0.902). Significant differences were observed between models (p < 0.001), with a consistent ranking of Claude > ChatGPT > Gemini. Differences were observed across all evaluation criteria, with large effect sizes (Kendall's W up to 0.93). AI-generated graphical abstracts demonstrate moderate-to-high quality but remain heterogeneous across models. While promising as assistive tools, their use requires expert validation to ensure scientific accuracy and interpretative safety.
PMID:
42679238
Bibliographic data and abstract were imported from PubMed on 02 Sep 2026.
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