ArticleNPJ digital medicine2026
A systematic review of AI for predicting glaucoma progression: challenges and recommendations towards clinical implementation.
Article in NPJ digital medicine, 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.
- Progress in the Synthesis of Evidence in Glaucoma: What We Know and the Gaps that Remain.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- Teleophthalmology and Teleglaucoma in Clinical Practice: Attitudes of Ophthalmologists in Bulgaria.Healthcare (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
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Glaucoma is the leading cause of irreversible blindness worldwide with heterogeneous progression rates. Artificial Intelligence (AI) may enable accurate progression predictions in clinical practice. We conducted a systematic review to survey quantitative AI performance and examine strengths and shortfalls in current AI approaches with future clinical implementation in mind. Two reviewers independently screened studies in English from MEDLINE, Embase, Web of Science, Cochrane CENTRAL and arXiv since 2014 and performed risk of bias assessment on eligible studies using QUADAS-2. 46 reports of 43 unique studies demonstrated moderate to good performance in predicting glaucoma conversion, biological deterioration and progression to surgery. Several challenges for clinical translation remain, including inconsistent reporting, limitations and heterogeneity in study design and poor AI generalisability and transparency. We encourage future studies to adopt robust study design and transparent reporting and propose the first glaucoma-specific list of recommended practices and reporting items for future clinical implementation.
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.