ArticleIndian journal of ophthalmology2022
Acceptability of artificial intelligence-based retina screening in general population.
Article in Indian journal of ophthalmology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it, 25 citations in OpenAlex.
- Transformative applications of oculomics-based AI approaches in the management of systemic diseases: A systematic review.Journal of advanced research · 2025Pooled it
- Impact of AI-Assisted Diagnosis on American Patients' Trust in and Intention to Seek Help From Health Care Professionals: Randomized, Web-Based Survey Experiment.Journal of medical Internet research · 2025Trial
- Manhattan Vision Screening and Follow-Up Study: (NYC-SIGHT)Tele-Retinal Image Findings and Importance of Photography.Telemedicine journal and e-health : the official journal of the American Telemedicine Association · 2024Trial
- Patient satisfaction in tele-ophthalmology: systematic review and meta-analysis.Eye (London, England) · 2026Review
- Artificial Intelligence in Prehospital Tele-Emergency Medicine: A Survey of Acceptance and Attitudes.Healthcare (Basel, Switzerland) · 2026Article
- Teleophthalmology for Retinal Disease Screening and Referral in Low- and Middle-Income Countries: A Case Overview of India.International ophthalmology · 2026Review
- Constructs Influencing Patient Perceptions of Use of AI in Medical Imaging Analysis: Systematic Review.Interactive journal of medical research · 2026Review
- AI-based fundus screening of teachers: fundus tessellated density as a myopia biomarker and occupational associations.BMC public health · 2026Observational
- Prospective real-world implementation of deep learning systems in healthcare: a systematic review guided by implementation science.NPJ digital medicine · 2026Article
- Patient Perspectives on Artificial Intelligence-Based Diabetic Retinopathy Screening at an Urban US Medical Center.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- The acceptance of ophthalmic artificial intelligence for eye diseases: a literature review and qualitative analysis.Eye (London, England) · 2025Review
- Implementation of A New, Mobile Diabetic Retinopathy Screening Model Incorporating Artificial Intelligence in Remote Western Australia.The Australian journal of rural health · 2025Article
- Comparing code-free and bespoke deep learning approaches in ophthalmology.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Review
- Trust in and Acceptance of Artificial Intelligence Applications in Medicine: Mixed Methods Study.JMIR human factors · 2024Article
- Artificial intelligence in retinal disease: clinical application, challenges, and future directions.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2023Review
- Artificial Intelligence-Based Consumer Health Informatics Application: Scoping Review.Journal of medical Internet research · 2023Article
- Commentary: Artificial intelligence-based screening of retina.Indian journal of ophthalmology · 2022Article
- Artificial intelligence in ophthalmology - Machines think!Indian journal of ophthalmology · 2022Article
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: A deep learning system (DLS) using artificial intelligence (AI) is emerging as a very promising technology in the future of healthcare diagnostics. While the concept of telehealth is emerging in every field of medicine, AI assistance in diagnosis can become a great tool for successful screening in telemedicine and teleophthalmology. The aim of our study was to assess the acceptability of AI-based retina screening. Methods: This was a prospective non-randomized study performed in the outpatient department of a tertiary eye care hospital. Patients older than 18 years who came for a regular eye check-up or a routine retina screening were recruited in the study. Fundus images of the posterior pole were captured on fundus on a phone camera (REMIDIO Results: We found that 90.4% were willing for an AI-based fundus screening; 96.2% were satisfied with AI-based screening. Patients with diabetes (P = 0.03) and the male population (P = 0.029) were more satisfied with the AI-based screening. The majority (i.e., 97.1%) felt that AI-based screening gave them a better understanding of their eye condition and 37.5% felt that AI-based retina screening prior to a doctor's visit can help in routine screening. Conclusion: Considering the current COVID-19 pandemic situation across the globe, this study highlights the importance of AI-based telescreening and positive patient approach toward this technology.
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Registered trials
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.