Evidence map›Paper›PMID 37869018›Full record

ReviewOphthalmology science

Clinical Utility of Smartphone Applications in Ophthalmology: A Systematic Review.

Ken Nagino, Jaemyoung Sung, Akie Midorikawa-Inomata, Atsuko Eguchi, Keiichi Fujimoto, Yuichi Okumura, Maria Miura, Alan Yee, Shokirova Hurramhon, Kenta Fujio and 8 more

Open access · goldAbstract readReview
In one paragraph

Review in Ophthalmology science. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed, 1 pooled it
5.7field-weighted citation impact, top 3% of its field
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

15 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Binocular Vision Anomalies: An Update on Prevalence, Impact, Technological and Treatment Developments and Current Challenges.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026
    Review
  5. Review
  6. Review
  7. Observational
  8. Article
  9. AI-Powered Mobile App for Nuclear Cataract Detection.Sensors (Basel, Switzerland) · 2025
    Article
  10. Article
  11. Observational
  12. Article
  13. Article
  14. Article
  15. 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

18 authors at 2 institutions in 3 countries.

Ken NaginoDepartment of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Jaemyoung SungDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Akie Midorikawa-InomataDepartment of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Atsuko EguchiDepartment of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Keiichi FujimotoDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Yuichi OkumuraDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Maria MiuraDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Alan YeeDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Shokirova HurramhonDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Kenta FujioDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Yasutsugu AkasakiDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Kunihiko HirosawaDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Tianxiang HuangDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Mizu OhnoDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Yuki MorookaDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Xinrong ZouDepartment of Ophthalmology, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Hiroyuki KobayashiDepartment of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Takenori InomataDepartment of Hospital Administration, Juntendo University Graduate School of Medicine, Tokyo, Japan.
Juntendo University · JPUniversity of South Florida · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Topic: Numerous smartphone applications have been devised for diagnosis, treatment, and symptom management in ophthalmology. Despite the importance of systematic evaluation of the purpose, target disease, effectiveness, and utility of smartphone applications to their effective utilization, few studies have formally evaluated their validity, reliability, and clinical utility. Clinical Relevance: This report identifies smartphone applications with potential for clinical implementation in ophthalmology and summarizes the evidence on their practical utility. Methods: We searched PubMed and EMBASE on July 28, 2022, for articles reporting original data on the effectiveness of treatment, disease detection, diagnostic accuracy, disease monitoring, and usability of smartphone applications in ophthalmology published between January 1, 1987, and July 25, 2022. Their quality was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. Results: The initial search yielded 510 articles. After removing 115 duplicates and 285 articles based on inclusion and exclusion criteria, the full texts of the remaining 110 articles were reviewed. Furthermore, 71 articles were included in the final qualitative synthesis. All studies were determined to be of high (87.3%) or moderate (12.7%) quality. In terms of respective application of interest, 24 (33.8%) studies assessed diagnostic accuracy, 17 (23.9%) assessed disease detection, and 3 (4.2%) assessed intervention efficacy. A total of 48 smartphone applications were identified, of which 27 (56.3%) were publicly available. Seventeen (35.4%) applications included functions for ophthalmic examinations, 13 (27.1%) included functions aimed at disease detection, 10 (20.8%) included functions to support medical personnel, five (10.4%) included functions related to disease education, and three (6.3%) included functions to promote treatment adherence for patients. The largest number of applications targeted amblyopia (18.8%), followed by retinal disease (10.4%). Two (4.2%) smartphone applications reported significant efficacy in treating diseases. Conclusion: In this systematic review, a comprehensive appraisal is presented on studies related to diagnostic accuracy, disease detectability, and efficacy of smartphone applications in ophthalmology. Forty-eight applications with potential clinical utility are identified. Appropriate smartphone applications are expected to enable early detection of undiagnosed diseases via telemedicine and prevent visual dysfunction via remote monitoring of chronic diseases. Financial Disclosures: Proprietary or commercial disclosure may be found after the references.

Indexed as

Early detection of undiagnosed diseasesOphthalmologyPotential clinical utilityRemote monitoringSmartphone applications

Identifiers

PMID37869018
PMCPMC10587618
OpenAlexW4379058836

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.