ReviewKlinische Monatsblatter fur Augenheilkunde2024
Role of Artificial Intelligence in Retinal Diseases.
Review in Klinische Monatsblatter fur Augenheilkunde, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 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
4 citing papers in PubMed.
- Comparison of Clinician Review Time and Confidence with Optical Coherence Tomography Digital versus Print-Based Workflows: A Pilot Observer Study.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- Artificial Intelligence Driven Adjusted OCT-Based Classification System for Diabetic Macular Edema (AIDME).Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- Automated Diabetic Retinopathy Screening in Out-patient Diabetes Care - Comparison of Two Artificial Intelligence Algorithms: RetCAD and OphtAI.Klinische Monatsblatter fur Augenheilkunde · 2025Article
- Real-world faricimab switch in France: artificial intelligence-based detection of changes in exudative signs in difficult-to-treat neovascular age-related macular degeneration.BMJ open ophthalmology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has already found its way into ophthalmology, with the first approved algorithms that can be used in clinical routine. Retinal diseases in particular are proving to be an important area of application for AI, as they are the main cause of blindness and the number of patients suffering from retinal diseases is constantly increasing. At the same time, regular imaging using high-resolution modalities in a standardised and reproducible manner generates immense amounts of data that can hardly be processed by human experts. In addition, ophthalmology is constantly experiencing new developments and breakthroughs that require a re-evaluation of patient management in routine clinical practice. AI is able to analyse these volumes of data efficiently and objectively and also provide new insights into disease progression and therapeutic mechanisms by identifying relevant biomarkers. AI can make a significant contribution to screening, classification and prognosis of various retinal diseases and can ultimately be a clinical decision support system, that significantly reduces the burden on both everyday clinical practice and the healthcare system, by making more efficient use of costly and time-consuming resources.
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.