SynthesisJournal of advanced research2025
Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.
Synthesis in Journal of advanced research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- From fundus to filtration: AI-driven retinal phenotyping as a framework for non-invasive prediction of kidney pathological categories (the "virtual renal biopsy" concept)- a narrative review.International urology and nephrology · 2026Review
- A decade of artificial intelligence research in ophthalmology: Global trends and transferable insights for medical AI.PLOS digital health · 2026Article
- Evolution of Oculomics: From Naked Eye Observations to Artificial Intelligence over 100 Years.Ophthalmology science · 2026Article
- Artificial Intelligence-Enhanced Multi-Algorithm R Shiny Application for Predictive Modeling and Analytics: Case Study of Alzheimer Disease Diagnostics.JMIR aging · 2025Article
- Accelerated Biological Aging in Exfoliation Glaucoma Assessed by Fundus-Derived Predicted Age and Advanced Glycation End Products.International journal of molecular sciences · 2025Article
- Fundus-Derived Predicted Age Acceleration in Glaucoma Patients Using Deep Learning and Propensity Score-Matched Controls.Journal of clinical medicine · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSystemic diseases, such as cardiovascular and cerebrovascular conditions, pose significant global health challenges due to their high mortality rates. Early identification and intervention in systemic diseases can substantially enhance their prognosis. However, diagnosing systemic diseases often necessitates complex, expensive, and invasive tests, posing challenges in their timely detection. Therefore, simple, cost-effective, and non-invasive methods for the management (such as screening, diagnosis, and monitoring) of systemic diseases are needed to reduce associated comorbidities and mortality rates. AIM OF THE REVIEW: This systematic review examines the application of artificial intelligence (AI) algorithms in managing systemic diseases by analyzing ophthalmic features (oculomics) obtained from convenient, affordable, and non-invasive ophthalmic imaging. KEY SCIENTIFIC CONCEPTS OF REVIEW: Our analysis demonstrates the promising accuracy of AI in predicting systemic diseases. Subgroup analysis reveals promising capabilities of oculomics-based AI for disease staging, while caution is warranted due to the possible overestimation of AI capabilities in low-quality studies. These systems are cost-effective and safe, with high rates of acceptance among patients and clinicians. This review underscores the potential of oculomics-based AI approaches in revolutionizing the management of systemic diseases.
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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.