ReviewGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2026
Artificial intelligence in ophthalmology clinical trials: a narrative review.
Review in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Dynamic Oculomics for Diagnostic Monitoring: Personal Ocular Trends, Risk Stratification and Treatment Response.Diagnostics (Basel, Switzerland) · 2026Review
- Deep Learning-Based Choroidal Boundary Detection in Geographic Atrophy Using Spectral-Domain Optical Coherence Tomography.Diagnostics (Basel, Switzerland) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) is progressively advancing across various medical specialties, particularly ophthalmology, to evaluate medical data, prevent diseases, and facilitate individualized treatment strategies. This narrative review investigates the current state of AI applications in ophthalmology clinical trials, focusing on patient monitoring, treatment optimization, and the diagnosis of prevalent ocular disorders. The implementation of AI techniques, such as deep learning and machine learning, improves the precision and efficacy of the process by evaluating ocular imaging data, such as OCTs, to diagnose various conditions, including diabetic retinopathy, age-related macular degeneration, glaucoma, and cataracts. Furthermore, AI has transformed patient recruiting, data management, and outcome assessment in clinical trials. In resource-limited settings, the combination of AI and teleophthalmology resulted in greater access to care. Despite its many positive aspects, we must address challenges like data bias, transparency, and ethical concerns to ensure the safe and equitable implementation of AI. As AI progresses, it has the capability to transform ophthalmic care and research, significantly improving patient outcomes and worldwide healthcare accessibility.
Indexed as
Identifiers
41117921What 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.