ArticleFrontiers in medicine2026
Learning ophthalmic anatomy with AI-generated visual resource: the moderating role of educational background.
Article in Frontiers in medicine, 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
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Declining ophthalmology teaching hours necessitate efficient instructional tools. While generative AI frequently produces structural deviations, these variations can be strategically repurposed as valuable stimuli for comparative learning. This study evaluated the effects of an AI-assisted comparative exercise versus conventional anatomical labeling on knowledge acquisition, learner satisfaction, and cognitive workload. Methods: We conducted a quasi-experimental 2 × 2 study with 121 sophomores from two universities in Shanghai, China. Following a standardized 20-min ophthalmic anatomy lecture, intact classes were assigned to either a conventional diagram-labeling task or an AI-assisted comparative exercise. The AI condition included three anatomically correct reference images paired with three expert-curated AI-generated anatomical variants, produced through a systematic expert-in-the-loop approach using Gemini 3.0 Pro. Outcomes comprised baseline and post-intervention knowledge tests, a 5-item satisfaction questionnaire, and the NASA Task Load Index. Results: After baseline adjustment and correction for planned comparisons, no statistically significant AI-versus-conventional difference in post-test knowledge scores was detected (all Conclusion: A brief AI-assisted comparative exercise did not demonstrate a statistically conclusive advantage in immediate knowledge outcomes, but was associated with higher satisfaction and better self-assessed performance among non-medical students without increasing composite NASA-TLX scores. Carefully curated AI-generated anatomical variants may therefore serve as a structured adjunct for novice ophthalmic anatomy learning.
Indexed as
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.