Evidence map›Paper›PMID 40539006›Full record

ReviewPediatric investigation2025

Application of artificial intelligence in myopia prevention and control.

Nan Liu, Li Li, Jifeng Yu

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Pediatric investigation, 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. Article
  3. Review
  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

3 authors.

Nan LiuDepartment of Ophthalmology Beijing Children's Hospital Capital Medical University National Center for Children's Health Key Laboratory of Major Diseases in Children Ministry of Education Beijing China.ORCID https://orcid.org/0000-0002-3099-7839
Li LiDepartment of Ophthalmology Beijing Children's Hospital Capital Medical University National Center for Children's Health Key Laboratory of Major Diseases in Children Ministry of Education Beijing China.
Jifeng YuDepartment of Ophthalmology Beijing Children's Hospital Capital Medical University National Center for Children's Health Key Laboratory of Major Diseases in Children Ministry of Education Beijing China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The global incidence of myopia is increasing, and high myopia increases the risk of pathological myopia, which can lead to irreversible visual impairment, posing a significant global health concern. Artificial intelligence (AI) may be a solution to the myopia pandemic, with potential applications in early identification, risk stratification, progression prediction, and timely intervention to address unmet needs. AI has been developed to detect, diagnose, and predict the progression of myopia in both children and adults. In this review, the current state of AI technology applications in the field of myopia has been comprehensively reviewed, and the challenges, current development status, and future directions of AI have also been discussed, which hold great significance for the further application of AI in myopia management.

Indexed as

Artificial intelligenceDeep learningMachine learningMyopia

Identifiers

PMID40539006
PMCPMC12175636

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.