ArticleCureus2024
Assessing the Role of Artificial Intelligence in the Creation of Patient Educational Videos for Corneal Refractive Surgery.
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, 1 of them a synthesis 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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- AI-generated videos in medical education: systematic review.BMJ open quality · 2025Pooled it
- AI-generated videos in medical science popularization and education: a scoping review.Frontiers in public health · 2026Article
- Detecting Sociodemographic Biases in the Content and Quality of Large Language Model-Generated Nursing Care: Cross-Sectional Simulation Study.Journal of medical Internet research · 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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeWe aim to assess the ability of artificial intelligence (AI) to generate patient educational videos for various corneal refractive surgeries.
methodsThree AI text-to-video platforms (InVideo (San Francisco, CA), ClipTalk (San Francisco, CA), and EasyVid (Los Angeles, CA)) were used to create patient educational videos for laser-assisted in situ keratomileusis (LASIK), photorefractive keratectomy (PRK), and small incision lenticule extraction (SMILE), respectively. Videos for LASIK and PRK from the American Academy of Ophthalmology (AAO) and a SMILE video from Zeiss served as controls for each surgery. A three-point grading system (from zero to three, with zero being the worst and three being the best in each category) was used to compare videos in terms of "image accuracy," "script accuracy," "image clarity," and "script alignment."
resultsIn terms of image accuracy, the control videos outperformed InVideo, EasyVid, and ClipTalk for LASIK (3 versus 0.667 versus 0 versus 0; p<0.005), PRK (3 versus 1 versus 0.33 versus 0; p<0.05 for InVideo, p<0.005 all), and SMILE (3 versus 0.33 versus 0 versus 0.33; p<0.005), respectively. With a few exceptions, all three AI models performed similarly to the control videos in terms of script accuracy, image clarity, and script alignment.
conclusionIn their current state, AI text-to-video generators can produce surgical educational videos for patients with accurate script narration and high image clarity, although these platforms are not yet capable of producing medically accurate images to go along with these scripts. Further improvements in the medical accuracy of these images must be made to make these videos more appropriate for patient consumption.
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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.