ReviewPharmaceuticals (Basel, Switzerland)2025
Rethinking Clinical Trials in Age-Related Macular Degeneration: How AI-Based OCT Analysis Can Support Successful Outcomes.
Review in Pharmaceuticals (Basel, Switzerland), 2025. 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.
- Deep Learning-Based Choroidal Boundary Detection in Geographic Atrophy Using Spectral-Domain Optical Coherence Tomography.Diagnostics (Basel, Switzerland) · 2026Article
- Comparison of choroidal hypertransmission and retinal pigment epithelium loss for quantification of geographic atrophy across commonly used SD-OCT devices.Scientific reports · 2026Article
- Artificial intelligence for precision therapeutics in age-related macular degeneration: current advances, challenges, and future directions.Frontiers in artificial intelligence · 2026Review
- Integrative Landscape of Dry AMD Pathogenesis, Models, and Emerging Therapeutic Strategies.International journal of molecular sciences · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Age-related macular degeneration (AMD) is a leading cause of blindness in the developed world. Due to an aging population, its prevalence is expected to increase, making novel and optimized therapy options imperative. However, both late-stage forms of the disease, neovascular AMD (nAMD) and geographic atrophy (GA), exhibit considerable variability in disease progression and treatment response, complicating the evaluation of therapeutic efficacy and making it difficult to design clinical trials that are both inclusive and statistically robust. Traditional trial designs frequently rely on generalized endpoints that may not fully capture the nuanced benefits of treatment, particularly in diseases like GA, where functional improvements can be gradual or subtle. Artificial intelligence (AI) has the potential to address these issues by identifying novel, condition-specific biomarkers or endpoints, enabling precise patient stratification and improving recruitment strategies. By providing an overview of the advances and application of AI-based optical coherence tomography analysis in the context of AMD clinical trials, this review highlights the transformative potential of AI in optimizing clinical trial outcomes for patients with nAMD or GA secondary to AMD.
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.