ReviewGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2022
Analysis of international publication trends in artificial intelligence in ophthalmology.
Review in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed.
- Selecting the Right AI Algorithm for the Job: A Guide for Navigating the AI Jungle in Ophthalmology.Ophthalmology and therapy · 2025Article
- Predicting visual acuity of treated ocular trauma based on pattern visual evoked potentials by machine learning models.Frontiers in cell and developmental biology · 2025Article
- Bibliometric analysis of the 3-year trends (2018-2021) in literature on artificial intelligence in ophthalmology and vision sciences.BMJ health & care informatics · 2024Review
- Application of Deep Learning Methods in a Moroccan Ophthalmic Center: Analysis and Discussion.Diagnostics (Basel, Switzerland) · 2023Article
- Using Artificial Intelligence to Analyse the Retinal Vascular Network: The Future of Cardiovascular Risk Assessment Based on Oculomics? A Narrative Review.Ophthalmology and therapy · 2023Review
- A bibliometric analysis of systematic reviews and meta-analyses in ophthalmology.Frontiers in medicine · 2023Article
- Artificial intelligence assisted pterygium diagnosis: current status and perspectives.International journal of ophthalmology · 2023Article
- Artificial intelligence in ophthalmology.International journal of ophthalmology · 2023Article
- Article
- Publication trends of artificial intelligence in retina in 10 years: Where do we stand?Frontiers in medicine · 2022Article
- Systematic Bibliometric and Visualized Analysis of Research Hotspots and Trends on the Application of Artificial Intelligence in Ophthalmic Disease Diagnosis.Frontiers in pharmacology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeArtificial intelligence (AI) has entered the field of medicine, and ophthalmology is no exception. The objective of this study was to report on scientific production and publication trends, to identify journals, countries, international collaborations, and major MeSH terms involved in AI in ophthalmology research.
methodsScientometric methods were used to evaluate global scientific production and development trends in AI in ophthalmology using PubMed and the Web of Science Core Collection.
resultsA total of 1356 articles were retrieved over the period 1966-2019. The yearly growth of AI in ophthalmology publications has been 18.89% over the last ten years, indicating that AI in ophthalmology is a very attractive topic in science. Analysis of the most productive journals showed that most were specialized in computer and medical systems. No journal was found to specialize in AI in ophthalmology. The USA, China, and the UK were the three most productive countries. The study of international collaboration showed that, besides the USA, researchers tended to collaborate with peers from neighboring countries. Among the twenty most frequent MeSH terms retrieved, there were only four related to clinical topics, revealing the retina and glaucoma as the most frequently encountered subjects of interest in AI in ophthalmology. Analysis of the top ten Journal Citation Reports categories of journals and MeSH terms for articles confirmed that AI in ophthalmology research is mainly focused on engineering and computing and is mainly technical research related to computer methods.
conclusionsThis study provides a broad view of the current status and trends in AI in ophthalmology research and shows that AI in ophthalmology research is an attractive topic focusing on retinal diseases and glaucoma. This study may be useful for researchers in AI in ophthalmology such as clinicians, but also for scientists to better understand this research topic, know the main actors in this field (including journals and countries), and have a general overview of this research theme.
Indexed as
Identifiers
34999946What 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.