ArticleAsia-Pacific journal of ophthalmology (Philadelphia, Pa.)
Development of oculomics artificial intelligence for cardiovascular risk factors: A case study in fundus oculomics for HbA1c assessment and clinically relevant considerations for clinicians.
Article in Asia-Pacific journal of ophthalmology (Philadelphia, Pa.). The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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.
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Who cites it
3 citing papers in PubMed.
- Time and person sensitive foundation model for disease prediction and risk stratification.NPJ digital medicine · 2026Article
- AI-driven saliency-guided retinal vessel segmentation framework for sustainable digital pathology.Frontiers in medicine · 2026Article
- Research advances on artificial intelligence assisted diagnosis and risk assessment in cardiovascular disease using retinal imaging.Frontiers in cardiovascular medicine · 2025Review
Corrections and comments
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Authors and funding
17 authors.
Funding
Abstract
Artificial Intelligence (AI) is transforming healthcare, notably in ophthalmology, where its ability to interpret images and data can significantly enhance disease diagnosis and patient care. Recent developments in oculomics, the integration of ophthalmic features to develop biomarkers for systemic diseases, have demonstrated the potential for providing rapid, non-invasive methods of screening leading to enhance in early detection and improve healthcare quality, particularly in underserved areas. However, the widespread adoption of such AI-based technologies faces challenges primarily related to the trustworthiness of the system. We demonstrate the potential and considerations needed to develop trustworthy AI in oculomics through a pilot study for HbA1c assessment using an AI-based approach. We then discuss various challenges, considerations, and solutions that have been developed for powerful AI technologies in the past in healthcare and subsequently apply these considerations to the oculomics pilot study. Building upon the observations in the study we highlight the challenges and opportunities for advancing trustworthy AI in oculomics. Ultimately, oculomics presents as a powerful and emerging technology in ophthalmology and understanding how to optimize transparency prior to clinical adoption is of utmost importance.
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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 Socratic graph.