Artificial Intelligence-generated Clinical Images for Plastic Surgery Education: A Prospective Randomized Double-blind Study.
- 1From the Plastic and Reconstructive Surgery Unit, Public Health Department, Università degli Studi di Napoli Federico II, Napoli, Italy.
Abstract
Background
High-quality clinical images are essential for plastic surgery education and board examinations, but privacy concerns and limited case diversity constrain their use. The educational validity of generative artificial intelligence (AI) images remains uncertain.Methods
In a prospective randomized double-blind study, 47 plastic surgeons (40 residents, 7 senior surgeons) each evaluated 60 randomized AI-generated and real clinical images depicting board-style scenarios. Participants rated realism and educational usefulness (0-10), suitability for replacing real photographs (yes/no), and artificial elements (yes/no). Participant-level means and proportions were compared using paired t tests and Wilcoxon tests. Test-retest reliability at 1 month was assessed using intraclass correlation coefficients.Results
Realism ratings did not differ significantly between AI-generated and real images (8.46 versus 8.36; P = 0.5044). AI-generated images received higher educational usefulness ratings (9.13 versus 8.75; P = 0.0035). Replacement endorsement did not differ significantly (0.95 versus 0.93; P = 0.4140). Artificial elements were reported less frequently in AI-generated images (0.02 versus 0.15; P < 0.0001). Findings were similar across experience levels. All aggregated measures showed good test-retest agreement (intraclass correlation coefficients > 0.8).Conclusions
AI-generated images received high realism ratings and slightly higher educational usefulness ratings than real photographs in this cohort. These findings support curated AI imagery as a potentially privacy-preserving educational resource. Diagnostic performance, learning outcomes, and demographic bias require evaluation before widespread curricular or examination use.
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BibTeX
@article{schonauer2026artificial,
title = {{Artificial Intelligence-generated Clinical Images for Plastic Surgery Education: A Prospective Randomized Double-blind Study.}},
author = {Fabrizio Schonauer and Annachiara Cavaliere and Tatiana Zaporojan and Ludovica De Gregorio and Concetta Errico and Francesco D'Andrea and Simone La Padula},
journal = {Plastic and reconstructive surgery. Global open},
year = {2026},
month = oct,
doi = {10.1097/gox.0000000000008221},
url = {https://europepmc.org/article/MED/42948812},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Artificial Intelligence-generated Clinical Images for Plastic Surgery Education: A Prospective Randomized Double-blind Study.
AU - Fabrizio Schonauer
AU - Annachiara Cavaliere
AU - Tatiana Zaporojan
AU - Ludovica De Gregorio
AU - Concetta Errico
AU - Francesco D'Andrea
AU - Simone La Padula
PY - 2026
DA - 2026/10/27
JO - Plastic and reconstructive surgery. Global open
DO - 10.1097/gox.0000000000008221
UR - https://europepmc.org/article/MED/42948812
AB - Background High-quality clinical images are essential for plastic surgery education and board examinations, but privacy concerns and limited case diversity constrain their use. The educational validity of generative artificial intelligence (AI) images remains uncertain.Methods In a prospective randomized double-blind study, 47 plastic surgeons (40 residents, 7 senior surgeons) each evaluated 60 randomized AI-generated and real clinical images depicting board-style scenarios. Participants rated realism and educational usefulness (0-10), suitability for replacing real photographs (yes/no), and artificial elements (yes/no). Participant-level means and proportions were compared using paired t tests and Wilcoxon tests. Test-retest reliability at 1 month was assessed using intraclass correlation coefficients.Results Realism ratings did not differ significantly between AI-generated and real images (8.46 versus 8.36; P = 0.5044). AI-generated images received higher educational usefulness ratings (9.13 versus 8.75; P = 0.0035). Replacement endorsement did not differ significantly (0.95 versus 0.93; P = 0.4140). Artificial elements were reported less frequently in AI-generated images (0.02 versus 0.15; P < 0.0001). Findings were similar across experience levels. All aggregated measures showed good test-retest agreement (intraclass correlation coefficients > 0.8).Conclusions AI-generated images received high realism ratings and slightly higher educational usefulness ratings than real photographs in this cohort. These findings support curated AI imagery as a potentially privacy-preserving educational resource. Diagnostic performance, learning outcomes, and demographic bias require evaluation before widespread curricular or examination use.
SN - 2169-7574
ER -