ReviewClinical ophthalmology (Auckland, N.Z.)2023
Artificial Intelligence and Glaucoma: Going Back to Basics.
Review in Clinical ophthalmology (Auckland, N.Z.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 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
5 citing papers in PubMed.
- Development of deep learning model to screen for primary open-angle glaucoma in African ancestry individuals.NPJ digital medicine · 2026Article
- Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection.Bioengineering (Basel, Switzerland) · 2024Review
- Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening.Journal of the National Cancer Institute · 2024Article
- Evaluating the strengths and limitations of multimodal ChatGPT-4 in detecting glaucoma using fundus images.Frontiers in ophthalmology · 2024Article
- Editorial: The diagnoses of glaucoma in the era of artificial intelligence.Frontiers in ophthalmology · 2024Article
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
There has been a recent surge in the number of publications centered on the use of artificial intelligence (AI) to diagnose various systemic diseases. The Food and Drug Administration has approved several algorithms for use in clinical practice. In ophthalmology, most advances in AI relate to diabetic retinopathy, which is a disease process with agreed upon diagnostic and classification criteria. However, this is not the case for glaucoma, which is a relatively complex disease without agreed-upon diagnostic criteria. Moreover, currently available public datasets that focus on glaucoma have inconstant label quality, further complicating attempts at training AI algorithms efficiently. In this perspective paper, we discuss specific details related to developing AI models for glaucoma and suggest potential steps to overcome current limitations.
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.