Evidence map›Paper›PMID 41879981›Full record

ReviewGraefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie2026

Artificial intelligence in pediatric myopia - a narrative review.

Neelam Pawar, Tos T J M Berendschot, Noël J C Bauer, Meenakshi R, Devendra Maheshwari, Binh Duong Giap, Nambi Nallasamy

Erratum issuedAbstract readReview
PubMed Publisher
In one paragraph

Review in Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

7 authors.

Neelam PawarAravind Eye Hospital and Post Graduate Institute of Ophthalmology, Tirunelveli, Tamil Nadu, 627001, India. neelamp@umich.edu.ORCID http://orcid.org/0000-0002-4014-5146
Tos T J M BerendschotUniversity Eye Clinic Maastricht, Maastricht University Medical Centre, Maastricht, the Netherlands.ORCID http://orcid.org/0000-0002-8101-939X
Noël J C BauerUniversity Eye Clinic Maastricht, Maastricht University Medical Centre, Maastricht, the Netherlands.
Meenakshi RAravind Eye Hospital and Post Graduate Institute of Ophthalmology, Tirunelveli, Tamil Nadu, 627001, India.ORCID http://orcid.org/0000-0002-9911-3042
Devendra MaheshwariAravind Eye Hospital and Post Graduate Institute of Ophthalmology, Tirunelveli, Tamil Nadu, 627001, India.
Binh Duong GiapKellogg Eye Center, Department of Ophthalmology & Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA.ORCID http://orcid.org/0000-0001-8211-106X
Nambi NallasamyKellogg Eye Center, Department of Ophthalmology & Visual Sciences, University of Michigan, Ann Arbor, Michigan, USA.ORCID http://orcid.org/0000-0001-7501-7198

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This review examines the promising potential of Artificial Intelligence (AI) in pediatric ophthalmology, specifically in the assessment, prediction, management, and treatment of myopia in children. The use of AI, particularly machine learning (ML) and deep learning (DL), in predicting myopia in children has garnered significant interest for its potential in early screening, detection, prognosis prediction, monitoring of anti-myopia treatment, personalized interventions, and proactive management strategies. This review aims to summarize the current literature on myopia and AI, presenting them as emerging trends and future directions in the pediatric population, and highlighting emerging strategies for the future of myopia management.

Indexed as

Artificial IntelligenceMyopiaChildHumansArtificial IntelligenceDeep learningMachine learningPediatric myopia

Identifiers

PMID41879981

What OpenQuestion holds

Textmetadata
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 OpenQuestion graph.