Evidence mapPaperPMID 40338607Full record

ReviewJAMA ophthalmology2025

Application of Artificial Intelligence to Deliver Healthcare From the Eye.

Robert N Weinreb, Aaron Y Lee, Sally L Baxter, Richard W J Lee, Theodore Leng, Michael V McConnell, Nevin W El-Nimri, David C Rhew

Abstract readReview
In one paragraph

Review in JAMA ophthalmology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
field-weighted citation impact
1 · What the graph read from 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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Robert N WeinrebViterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla.
Aaron Y LeeDepartment of Ophthalmology, School of Medicine, University of Washington, Seattle.
Sally L BaxterViterbi Family Department of Ophthalmology, University of California, San Diego, La Jolla.
Richard W J LeeNational Eye Institute, National Institutes of Health, Bethesda, Maryland.
Theodore LengDepartment of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California.
Michael V McConnellDivision of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, California.
Nevin W El-NimriTopcon Healthcare Inc, Oakland, New Jersey.
David C RhewHealth & Life Sciences, Microsoft, Seattle, Washington.

Funding

San Diego Biomedical Informatics Education & Research (SABER)T15LM011271 · UNIVERSITY OF CALIFORNIA, SAN DIEGO · 2025 to 2025
$453k
NLM NIH HHS T15 LM011271
6 · The paper itself

Abstract

Importance: Oculomics is the science of analyzing ocular data to identify, diagnose, and manage systemic disease. This article focuses on prescreening, its use with retinal images analyzed by artificial intelligence (AI), to identify ocular or systemic disease or potential disease in asymptomatic individuals. The implementation of prescreening in a coordinated care system, defined as Healthcare From the Eye prescreening, has the potential to improve access, affordability, equity, quality, and safety of health care on a global level. Stakeholders include physicians, payers, policymakers, regulators and representatives from industry, government, and data privacy sectors. Observations: The combination of AI analysis of ocular data with automated technologies that capture images during routine eye examinations enables prescreening of large populations for chronic disease. Retinal images can be acquired during either a routine eye examination or in settings outside of eye care with readily accessible, safe, quick, and noninvasive retinal imaging devices. The outcome of such an examination can then be digitally communicated across relevant stakeholders in a coordinated fashion to direct a patient to screening and monitoring services. Such an approach offers the opportunity to transform health care delivery and improve early disease detection, improve access to care, enhance equity especially in rural and underserved communities, and reduce costs. Conclusions and Relevance: With effective implementation and collaboration among key stakeholders, this approach has the potential to contribute to an equitable and effective health care system.

Indexed as

Artificial IntelligenceDelivery of Health CareEye DiseasesMass ScreeningOphthalmologyHumans

Identifiers

PMID40338607
PMCPMC13178808

What Socratic holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.