ReviewDiagnostics (Basel, Switzerland)2025
Artificial Intelligence Application in Cornea and External Diseases.
Review in Diagnostics (Basel, Switzerland), 2025. 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.
- From Morphology to Biomarkers: Optical Coherence Tomography in Corneal Disease.Diseases (Basel, Switzerland) · 2026Review
- A Review of the Use of Artificial Intelligence in Ophthalmology Imaging: Approximation to Ocular Histopathology.APMIS : acta pathologica, microbiologica, et immunologica Scandinavica · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Corneal diseases are a leading cause of blindness worldwide, although their early detection remains challenging due to subtle clinical presentations. Recent advances in artificial intelligence (AI) have shown promising diagnostic performance for anterior segment disorders. This narrative review summarizes current applications of AI in the detection of corneal conditions-including keratoconus (KC), dry eye disease (DED), infectious keratitis (IK), pterygium, Fuchs endothelial corneal dystrophy (FECD), and corneal transplantation. Many AI models report high accuracy on test datasets, comparable to, and in some studies exceeding, that of junior ophthalmologists. In addition to detection, AI systems can automate image labeling and support education and patient home monitoring. These findings highlight the potential of AI to improve early management and standardized classification of corneal diseases, supporting clinical practice and patient self-care.
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.