Evidence map›Paper›PMID 39354293›Full record

ReviewJournal of imaging informatics in medicine2025

Ocular Imaging Challenges, Current State, and a Path to Interoperability: A HIMSS-SIIM Enterprise Imaging Community Whitepaper.

Kerry E Goetz, Michael V Boland, Zhongdi Chu, Amberlynn A Reed, Shawn D Clark, Alexander J Towbin, Boonkit Purt, Kevin O'Donnell, Marilyn M Bui, Monief Eid and 3 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of imaging informatics in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Initiation of a global consortium to study the progression of age-related macular degeneration: RIMR AMD consortium report # 1.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026
    Article
  3. Article
  4. 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

13 authors.

Kerry E GoetzOffice of Data Science and Health Informatics, National Eye Institute, National Institute of Health, Bethesda, MD, USA. kerry.goetz@nih.gov.
Michael V BolandMassachusetts Eye and Ear, Harvard Medical School, Boston, MA, USA.
Zhongdi ChuVerana Health, Inc, San Francisco, CA, USA.
Amberlynn A ReedOffice of Data Science and Health Informatics, National Eye Institute, National Institute of Health, Bethesda, MD, USA.
Shawn D ClarkCybersecurity-Network Protection, Cleveland Clinic Foundation, Cleveland, OH, USA.
Alexander J TowbinDepartment of Radiology, Cincinnati Children's Hospital, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
Boonkit PurtUniformed Services University of the Health Sciences, Bethesda, MD, USA.
Kevin O'DonnellCanon Medical Research, Mayfield, KY, USA.
Marilyn M BuiDepartments of Pathology and Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, 33612, USA.
Monief EidMinistry of Health, Riyadh, Saudi Arabia.
Christopher J RothDuke Health, Durham, NC, USA.
Damien M LuvianoVirginia Tech Carilion School of Medicine, University of Virginia School of Medicine, Roanoke, VA, USA.
Les R FolioDiagnostic Imaging & Interventional Radiology, Department of Machine Learning, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Office-based testing, enhanced by advances in imaging technology, is routinely used in eye care to non-invasively assess ocular structure and function. This type of imaging coupled with autonomous artificial intelligence holds immense opportunity to diagnose eye diseases quickly. Despite the wide availability and use of ocular imaging, there are several factors that hinder optimization of clinical practice and patient care. While some large institutions have developed end-to-end digital workflows that utilize electronic health records, enterprise imaging archives, and dedicated diagnostic viewers, this experience has not yet made its way to smaller and independent eye clinics. Fractured interoperability practices impact patient care in all healthcare domains, including eye care where there is a scarcity of care centers, making collaboration essential among providers, specialists, and primary care who might be treating systemic conditions with profound impact on vision. The purpose of this white paper is to describe the current state of ocular imaging by focusing on the challenges related to interoperability, reporting, and clinical workflow.

Indexed as

Diagnostic ImagingEyeEye DiseasesHealth Information InteroperabilityArtificial IntelligenceElectronic Health RecordsHumansDICOMEnterprise imagingInformaticsInteroperabilityMedical imagingOphthalmologyOptometry

Identifiers

PMID39354293
PMCPMC12092316

What Socratic holds

Textmetadata
LicenceCC BY
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.