ReviewJournal of personalized medicine2024
Artificial Intelligence and Advanced Technology in Glaucoma: A Review.
Review in Journal of personalized medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
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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
15 citing papers in PubMed.
- Artificial intelligence in ophthalmology: From diagnostic accuracy to clinical application.World journal of methodology · 2026Review
- Clinical Performance and Implementation of AI-Enabled Paediatric Ophthalmic Screening, Triage, Diagnosis, and Surveillance in Primary, Community, and Referral-Linked Pathways: A Systematic Review.Diagnostics (Basel, Switzerland) · 2026Review
- A novel superpixel based Vision Transformer for improving interpretability in glaucoma screening.Scientific reports · 2026Article
- Neurosymbolic AI Framework for Explainable Retinal Disease Classification From OCT Images.Translational vision science & technology · 2026Article
- Neovascular glaucoma: pathophysiology, diagnosis, and contemporary management.Frontiers in medicine · 2026Review
- Recurrent Neural Networks with Integrated Gradients Explanation for Predicting the Hysteresis Behavior of Shape Memory Alloys.Sensors (Basel, Switzerland) · 2025Article
- Exploring AI's Potential in Papilledema Diagnosis to Support Dermatological Treatment Decisions in Rural Healthcare.Diagnostics (Basel, Switzerland) · 2025Article
- Preoperative predictive thresholds for successful canaloplasty in glaucoma patients.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Article
- Two Machine Learning Models to Economize Glaucoma Screening Programs: An Approach Based on Neural Nets.Journal of personalized medicine · 2025Article
- Assessment of Retinal Microcirculation in Primary Open-Angle Glaucoma Using Adaptive Optics and OCT Angiography: Correlation with Structural and Functional Damage.Journal of clinical medicine · 2025Article
- Artificial Intelligence in Glaucoma: Advances in Diagnosis, Progression Forecasting, and Surgical Outcome Prediction.International journal of molecular sciences · 2025Review
- Deep Learning in Glaucoma Detection and Progression Prediction: A Systematic Review and Meta-Analysis.Biomedicines · 2025Review
- Article
- Applications, image analysis, and interpretation of computer vision in medical imaging.Frontiers in radiology · 2025Review
- Meeting Challenges in the Diagnosis and Treatment of Glaucoma.Bioengineering (Basel, Switzerland) · 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
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGlaucoma is a leading cause of irreversible blindness worldwide, necessitating precise management strategies tailored to individual patient characteristics. Artificial intelligence (AI) holds promise in revolutionizing the approach to glaucoma care by providing personalized interventions.
aimThis review explores the current landscape of AI applications in the personalized management of glaucoma patients, highlighting advancements, challenges, and future directions.
methodsA systematic search of electronic databases, including PubMed, Scopus, and Web of Science, was conducted to identify relevant studies published up to 2024. Studies exploring the use of AI techniques in personalized management strategies for glaucoma patients were included.
resultsThe review identified diverse AI applications in glaucoma management, ranging from early detection and diagnosis to treatment optimization and prognosis prediction. Machine learning algorithms, particularly deep learning models, demonstrated high accuracy in diagnosing glaucoma from various imaging modalities such as optical coherence tomography (OCT) and visual field tests. AI-driven risk stratification tools facilitated personalized treatment decisions by integrating patient-specific data with predictive analytics, enhancing therapeutic outcomes while minimizing adverse effects. Moreover, AI-based teleophthalmology platforms enabled remote monitoring and timely intervention, improving patient access to specialized care.
conclusionsIntegrating AI technologies in the personalized management of glaucoma patients holds immense potential for optimizing clinical decision-making, enhancing treatment efficacy, and mitigating disease progression. However, challenges such as data heterogeneity, model interpretability, and regulatory concerns warrant further investigation. Future research should focus on refining AI algorithms, validating their clinical utility through large-scale prospective studies, and ensuring seamless integration into routine clinical practice to realize the full benefits of personalized glaucoma care.
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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.