ArticleOphthalmology. Glaucoma
Special Commentary: Using Clinical Decision Support Systems to Bring Predictive Models to the Glaucoma Clinic.
Article in Ophthalmology. Glaucoma. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
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
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The role of artificial intelligence in analysis of biofluid markers for diagnosis and management of glaucoma: A systematic review.European journal of ophthalmology · 2023Pooled it
- Real-World Barriers to and Facilitators of Implementing AI-Based Clinical Decision Support Systems: Scoping Review.JMIR medical informatics · 2026Article
- Data Volume and the Need for Clinical Decision Support in Glaucoma Care.Ophthalmology science · 2026Article
- Perceived Potential and Challenges of Supporting Coronary Artery Disease Treatment Decisions With AI: Qualitative Study.JMIR cardio · 2026Article
- Global research status in the treatment of glaucoma: a systematic bibliometric analysis.International journal of ophthalmology · 2026Article
- The explainable AI dilemma under knowledge imbalance in specialist AI for glaucoma referrals in primary care.NPJ digital medicine · 2025Article
- Development and Validation of PT-PENCIL: The Physical Therapy Frequency Clinical Decision Support Tool to Increase Hospital Discharge to Home.Physical therapy · 2025Article
- Applications of machine learning in glaucoma diagnosis based on tabular data: a systematic review.BMC biomedical engineering · 2025Article
- Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.Journal of medical systems · 2025Article
- Application of AI in the identification of gastrointestinal stromal tumors: a comprehensive analysis based on pathological, radiological, and genetic variation features.Frontiers in genetics · 2025Review
- Social determinants of health and health disparities in glaucoma: A review.Clinical & experimental ophthalmology · 2024Review
- A Nationwide Chronic Disease Management Solution via Clinical Decision Support Services: Software Development and Real-Life Implementation Report.JMIR medical informatics · 2024Article
- Deep Learning Identifies High-Quality Fundus Photographs and Increases Accuracy in Automated Primary Open Angle Glaucoma Detection.Translational vision science & technology · 2024Article
- Systematic User-centered Design of a Prototype Clinical Decision Support System for Glaucoma.Ophthalmology science · 2023Article
- Smoking Cessation May Reduce Risk of Visual Field Progression in Heavy Smokers.Journal of glaucoma · 2022Article
- Enabling a learning healthcare system with automated computer protocols that produce replicable and personalized clinician actions.Journal of the American Medical Informatics Association : JAMIA · 2021Article
- Interests and needs of eye care providers in clinical decision support for glaucoma.BMJ open ophthalmology · 2021Article
- Review
- The Frequency of Visual Field Testing in a US Nationwide Cohort of Individuals with Open-Angle Glaucoma.Ophthalmology. GlaucomaArticle
- Time to Identify Glaucoma Progression Given Typical Visual Field Testing Frequencies in a US Nationwide Insured Population.Ophthalmology. GlaucomaArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
Advances in the field of predictive modeling using artificial intelligence and machine learning have the potential to improve clinical care and outcomes, but only if the results of these models are presented appropriately to clinicians at the time they make decisions for individual patients. Clinical decision support (CDS) systems could be used to accomplish this. Modern CDS systems are computer-based tools designed to improve clinician decision making for individual patients. However, not all CDS systems are effective. Four principles that have been shown in other medical fields to be important for successful CDS system implementation are (1) integration into clinician workflow, (2) user-centered interface design, (3) evaluation of CDS systems and rules, and (4) standards-based development so the tools can be deployed across health systems.
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.