ArticleScientific reports2024
Comparative educational effectiveness of AI generated images and traditional lectures for diagnosing chalazion and sebaceous carcinoma.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 2 of them syntheses that pooled it.
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
6 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The impact of generative artificial intelligence on clinical skills and knowledge acquisition in medical undergraduates: a systematic review and meta-analysis.BMC medical education · 2026Pooled it
- Effectiveness of generative artificial intelligence-based teaching versus traditional teaching methods in medical education: a meta-analysis of randomized controlled trials.BMC medical education · 2025Pooled it
- Review
- Effects of Artificial Intelligence-Supported Education on Critical Thinking, Problem-Solving, Clinical Reasoning, and Decision-Making in Health Science Education: A Systematic Review of Interventional Studies.Advances in medical education and practice · 2026Review
- AI-Generated "Slop" in Online Biomedical Science Educational Videos: Mixed Methods Study of Prevalence, Characteristics, and Hazards to Learners and Teachers.JMIR medical education · 2025Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Sebaceous carcinoma is difficult to distinguish from chalazion due to their rarity and clinicians' limited experience. This study investigated the potential of AI-generated image training to improve diagnostic skills for these eyelid tumors compared to traditional video lecture-based education. Students from Orthoptics, Optometry, and Vision Research (n = 55) were randomly assigned to either an AI-generated image training group or a traditional video lecture group. Diagnostic performance was assessed using a 50-image quiz before and after the intervention. Both groups showed significant improvement in overall diagnostic accuracy (p < 0.001), with no significant difference between groups (p = 0.124). In the AI group, all 25 chalazion images showed improvement, while only 6 out of 25 sebaceous carcinoma images improved. The video lecture group showed improvement in 19 out of 25 chalazion images and 24 out of 25 sebaceous carcinoma images. The proportion of images with improved accuracy was significantly higher in the AI group for chalazion (P = 0.022) and in the video group for sebaceous carcinoma (P < 0.001). These findings suggest that AI-generated image training can enhance diagnostic skills for rare conditions, but its effectiveness depends on the quality and quantity of patient images used for optimization. Combining AI-generated image training with traditional video lectures may lead to more effective educational programs. Further research is needed to explore AI's potential in medical education and improve diagnostic skills for rare diseases.
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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.