ReviewThe British journal of ophthalmology2024
Generative artificial intelligence in ophthalmology: current innovations, future applications and challenges.
Review in The British journal of ophthalmology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ophthalmic Image Synthesis and Analysis with Generative Adversarial Network Artificial Intelligence.Journal of imaging informatics in medicine · 2026Pooled it
- Ethical considerations in generative artificial intelligence for synthetic data generation in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Portrayal of Cancer Patients in the Era of AI: A Content Analysis of Images Produced by Generative AI Tools.Health communication · 2026Article
- Epidemiology of cataract in China from 1990 to 2021: findings from the Global Burden of disease study 2021.BMC public health · 2026Article
- Boosting foundation models for rare eye disease diagnosis via a multimodal text-to-image generative framework.NPJ digital medicine · 2026Article
- Accuracy, readability, and bias of GPT-4o mini responses to oculoplastic patient questions.Frontiers in ophthalmology · 2026Article
- Generative Artificial Intelligence for Retinal Image Translation to Improve Glaucoma Screening With Deep Learning.Translational vision science & technology · 2025Article
- Generative artificial intelligence in ophthalmology: a scoping review of current applications, opportunities, and challenges.Eye (London, England) · 2025Article
- Feasibility analysis of generative artificial intelligence tools as a means of medical science popularization on urological diseases.Translational andrology and urology · 2025Article
- Artificial Intelligence in Ophthalmology: Acceptance, Clinical Integration, and Educational Needs in Switzerland.Journal of clinical medicine · 2025Article
- Enhancing medical students' diagnostic accuracy of infectious keratitis with AI-generated images.BMC medical education · 2025Article
- Article
- [Artificial intelligence in medicine-Opportunities and risks from an ethical perspective].Die Ophthalmologie · 2025Review
- Artificial intelligence in ophthalmology: opportunities, challenges, and ethical considerations.Medical hypothesis, discovery & innovation ophthalmology journal · 2025Review
- Blinking characteristics analyzed by a deep learning model and the relationship with tear film stability in children with long-term use of orthokeratology.Frontiers in cell and developmental biology · 2024Article
- Generative artificial intelligence in graduate medical education.Frontiers in medicine · 2024Review
- Generative artificial intelligence in ophthalmology research writing: A comprehensive review of applications, detection tools, and ethical considerations.Taiwan journal of ophthalmologyReview
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
Abstract
The rapid advancements in generative artificial intelligence are set to significantly influence the medical sector, particularly ophthalmology. Generative adversarial networks and diffusion models enable the creation of synthetic images, aiding the development of deep learning models tailored for specific imaging tasks. Additionally, the advent of multimodal foundational models, capable of generating images, text and videos, presents a broad spectrum of applications within ophthalmology. These range from enhancing diagnostic accuracy to improving patient education and training healthcare professionals. Despite the promising potential, this area of technology is still in its infancy, and there are several challenges to be addressed, including data bias, safety concerns and the practical implementation of these technologies in clinical settings.
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.