ArticleHealth science reports2025
Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis.
Article in Health science reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 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
1 citing paper in PubMed, 1 synthesis or guideline pooled it.
- Healthcare analytics perspectives on artificial intelligence-based retinopathy of prematurity screening: a bibliometric mapping study.Frontiers in research metrics and analytics · 2026Pooled it
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
Background and Aims: Retinopathy of prematurity (ROP) remains a leading cause of childhood blindness worldwide. In recent years, artificial intelligence (AI) has emerged as a powerful tool for the screening and management of ROP. This study aimed to investigate the evolving and longitudinal publication patterns related to AI in ROP using bibliometric methodologies. Methods: We conducted a descriptive analysis of AI in ROP documents retrieved from the Web of Science database up to September 10, 2023. Data analysis and visualization were performed using Bibliometrix and VOSviewer, covering publications, journals, authors, institutions, countries, collaboration networks, keywords, and trending topics. Results: Our analysis of 188 publications on AI in ROP revealed an average of 7.62 authors per document and a notable increase in annual publications since 2017. The United States (98/188), Oregon Health & Science University (66/188), Investigative Ophthalmology & Visual Science (29/188) and author Michael F. Chiang (60/188) led contributions. A prominent 21-country network emerged as the largest in country-level coauthorship. Key technical terms included "artificial intelligence," "deep learning," "machine learning," and "telemedicine," with a recent shift from "feature selection" to "deep learning," "machine learning" and "fundus images" in trending topics. Conclusion: Our bibliometric analysis highlights advancements in AI research on ROP, focusing on key publication characteristics, major contributors, and emerging trends. The findings indicate that AI in ROP is a rapidly growing field. Future studies should focus on addressing the clinical implementation and ethical concerns of AI in ROP.
Indexed as
Identifiers
What Socratic 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 Socratic graph.