ReviewIndian journal of ophthalmology2021
Using artificial intelligence for diabetic retinopathy screening: Policy implications.
Review in Indian journal of ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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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.
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.
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Who cites it
19 citing papers in PubMed, 36 citations in OpenAlex.
- The diagnostic accuracy of AI-assisted diabetic retinopathy screening in primary care: a prospective validation study.Scandinavian journal of primary health care · 2026Article
- Prospective Data Curation Enables High-Performance Artificial Intelligence for Diabetic Retinopathy Screening in a Resource-Limited Setting.Ophthalmology science · 2026Article
- Current status and solutions for AI ethics in ophthalmology: a bibliometric analysis.NPJ digital medicine · 2025Article
- A Narrative Review of Ethical Issues in the Use of Artificial Intelligence Enabled Diagnostics for Diabetic Retinopathy.Journal of evaluation in clinical practice · 2025Review
- Enabling Physicians to Make an Informed Adoption Decision on Artificial Intelligence Applications in Medical Imaging Diagnostics: Qualitative Study.Journal of medical Internet research · 2025Article
- The Role of Artificial Intelligence in the Diagnosis and Management of Diabetic Retinopathy.Journal of clinical medicine · 2025Review
- Validation of artificial intelligence algorithm LuxIA for screening of diabetic retinopathy from a single 45° retinal colour fundus images: the CARDS study.BMJ open ophthalmology · 2025Article
- AI-Assisted Screening for Diabetic Retinopathy and Fundus Abnormalities in a Large-Scale Physical Examination Population.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Diagnostic Accuracy of Automated Diabetic Retinopathy Image Assessment Software: IDx-DR and RetCAD.Ophthalmology and therapy · 2025Article
- The use of artificial intelligence to assess diabetic eye disease among the Greenlandic population.International journal of circumpolar health · 2024Article
- Evaluation of Systemic Risk Factors in Patients with Diabetes Mellitus for Detecting Diabetic Retinopathy with Random Forest Classification Model.Diagnostics (Basel, Switzerland) · 2024Article
- Recommendations for diabetic macular edema management by retina specialists and large language model-based artificial intelligence platforms.International journal of retina and vitreous · 2024Article
- Recommendations for initial diabetic retinopathy screening of diabetic patients using large language model-based artificial intelligence in real-life case scenarios.International journal of retina and vitreous · 2024Article
- Review
- Efficacy of deep learning-based artificial intelligence models in screening and referring patients with diabetic retinopathy and glaucoma.Indian journal of ophthalmology · 2023Observational
- Examining the Role of Telemedicine in Diabetic Retinopathy.Journal of clinical medicine · 2023Review
- Diagnostic test accuracy of artificial intelligence in screening for referable diabetic retinopathy in real-world settings: A systematic review and meta-analysis.PLOS global public health · 2023Article
- Diabetic Retinopathy Screening Using Non-Mydriatic Fundus Camera in Primary Health Care Settings - A Multicenter Study from Saudi Arabia.International journal of general medicine · 2023Article
- Comprehensive Review on the Use of Artificial Intelligence in Ophthalmology and Future Research Directions.Diagnostics (Basel, Switzerland) · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has evolved over the last few years; its use in DR screening has been demonstrated in multiple evidences across the globe. However, there are concerns right from the data acquisition, bias in data, difficulty in comparing between different algorithm, challenges in machine learning, its application in different group of population, and human barrier to AI adoption in health care. There are also legal and ethical concerns related to AI. The tension between risks and concerns on one hand versus potential and opportunity on the other have driven a need for authorities to implement policies for AI in DR screening to address these issues. The policy makers should support and facilitate research and development of AI in healthcare, but at the same time, it has to be ensured that the use of AI in healthcare aligns with recognized standards of safety, efficacy, and equity. It is essential to ensure that algorithms, datasets, and decisions are auditable and when applied to medical care (such as screening, diagnosis, or treatment) are clinically validated and explainable. Policy frameworks should require design of AI systems in health care that are informed by real-world workflow and human-centric design. Lastly, it should be ensured that healthcare AI solutions align with all relevant ethical obligations, from design to development to use and to be delivered properly in the real world.
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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.