ArticleCureus2024
Advancing Innovation in Medical Presentations: A Guide for Medical Educators to Use Images Generated With Artificial Intelligence.
Article in Cureus, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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
4 citing papers in PubMed.
- Generative AI-Assisted Medical Illustrations in Pediatric Neurosurgery : Workflows, Ethical Safeguards, and Journal Policy Considerations.Journal of Korean Neurosurgical Society · 2026Review
- Bias, representation, and clinical fidelity in AI-generated images for medical education: a systematic literature review.NPJ digital medicine · 2026Article
- Beyond Google Images: Crafting impactful images for presentations or clinical use with AI.Indian journal of psychiatry · 2025Article
- Evaluating the impact of interactive video-based case-based learning in clinical medical education: a randomized controlled trial.Frontiers in medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Multimedia learning theory suggests that the use of images in the classroom enhances both cognitive and constructive learning. Finding the appropriate images for medical presentations can be challenging due to copyright and privacy issues. Artificial intelligence (AI) image generation is a newly available technology that offers a great advantage to medical educators. This technical report presents a robust guide for beginners to learn how to effectively use AI to generate images relevant to their presentations using free tools. The 12 guide tips include adhering to multimedia theory, using cost-effective resources, recognizing the possibilities, learning the technique, mastering the jargon, expanding the natural language, using prompt templates, creating stories, employing humor, avoiding perfection, exploiting the versatility of applications, and recognizing the limitations and ethical issues. By working through the article and the supplementary material, readers will be able to produce images similar to those provided in the article and use them effectively in their medical presentations.
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.