ReviewInternational ophthalmology clinics2024
Implementation of Artificial Intelligence in Retinopathy of Prematurity Care: Challenges and Opportunities.
Review in International ophthalmology clinics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Optimizing retinopathy of prematurity screening in China using a single objective criterion: a 10-year retrospective analysis.Frontiers in pediatrics · 2026Article
- Sustaining Ophthalmic Practices for the Future: A High-Value Care Approach to Environmental Responsibility.Ophthalmology and therapy · 2025Review
- Neonatal retinal hemorrhage and retinopathy of prematurity: a critical review of current practices and future directions.Therapeutic advances in 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
8 authors.
Funding
Abstract
The diagnosis of retinopathy of prematurity (ROP) is primarily image-based and suitable for implementation of artificial intelligence (AI) systems. Increasing incidence of ROP, especially in low and middle-income countries, has also put tremendous stress on health care systems. Barriers to the implementation of AI include infrastructure, regulatory, legal, cost, sustainability, and scalability. This review describes currently available AI and imaging systems, how a stable telemedicine infrastructure is crucial to AI implementation, and how successful ROP programs have been run in both low and middle-income countries and high-income countries. More work is needed in terms of validating AI systems with different populations with various low-cost imaging devices that have recently been developed. A sustainable and cost-effective ROP screening program is crucial in the prevention of childhood blindness.
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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.